# Fine-Tune Information Extractor for NLP Tasks based on T5-Small
[](https://pepy.tech/project/FineTune-Information-Extractor-for-NLPTasks-based-T5-Small)
Fine-Tune Information Extractor for NLP Tasks based on T5-Small is a powerful library designed for fine-tuning the pre-trained `t5-small` model on custom information
extraction tasks. The library provides an intuitive interface for loading datasets, fine-tuning the T5 model, and exporting results efficiently.
In our [Kaggle](https://www.kaggle.com/code/mehrdadal/finetune-information-extractor-t5-small) example, we fine-tuned the model
using the [leminda-ai/s2orc_small](https://huggingface.co/datasets/leminda-ai/s2orc_small) dataset to extract key information, such as author names,
from academic articles. It is important to note that to achieve efficient performance and faster results,
users should leverage high-performance GPUs like the **P100** available on Kaggle. In contrast, using GPUs like the **T4** on platforms such as Google Colab may result
in slower training times and less efficient fine-tuning, which could affect overall output quality for more demanding tasks.
## Table of Contents
- [Key Features](#key-features)
- [Quick Start](#quick-start)
- [Fine-tuning the Model](#fine-tuning-the-model)
- [Model Loading and Inference](#model-loading-and-inference)
- [Results](#results)
- [Authors](#authors)
- [License](#license)
- [Example Usage](#example-usage)
## Key Features
- **T5 Fine-tuning**: Fine-tune the `t5-small` model for custom information extraction tasks.
- **Customizable Task Instructions**: Supports flexible task instructions (e.g., "Extract Authors", "Extract Keywords").
- **Text Preprocessing**: Combines and tokenizes multiple text columns for input into the model.
- **GPU Support**: Utilizes GPU acceleration for faster training.
## Quick Start
The primary interface for interacting with this library is the `InfoExtractionModel` class, which allows you to load data, fine-tune the model, and generate output for a given input text.
## Fine-tuning the Model
To fine-tune the `t5-small` model, you need to prepare three datasets: one for training, and one for evaluation. These files should be provided in Excel or CSV format.
The process involves selecting the correct text column and target information column for information extraction.
### 1. Prepare Train, Evaluation, and Test Files
You must provide three separate files for training, evaluation, and testing. Each file should include columns containing the text you want to extract information from (e.g., abstracts or articles) and the specific target information (e.g., authors, dates, keywords) to extract.
- **Train File**: Contains the data to train the model.
- **Evaluation File**: Contains the data for validating the model during training.
### 2. Load and Show Available Columns
The first step is to load the dataset and display the available columns to let the user select the columns for text and target information.
You can specify the path to each file, and the code will display the available columns so you can choose which one contains the text and which
one contains the target information.
```python
import pandas as pd
# Load the dataset (Excel or CSV)
train_file_path = input("Please provide the path for the training dataset file: ")
eval_file_path = input("Please provide the path for the evaluation dataset file: ")
# Load the training data to display columns
train_data = pd.read_excel(train_file_path) if train_file_path.endswith('.xlsx') else pd.read_csv(train_file_path)
# Show available columns
print("\n--- Available Columns in Training Dataset ---")
print(train_data.columns)
# Ask the user to specify the text and target columns
text_column = input("\nEnter the name of the column containing the text data (e.g., 'combined_text'): ").strip()
target_information_column = input("Enter the name of the column containing the target information (e.g., 'Authors'): ").strip()
# Extract text and target information from training and evaluation datasets
texts_train = train_data[text_column].tolist()
targets_train = train_data[target_information_column].tolist()
# Load the evaluation data
eval_data = pd.read_excel(eval_file_path) if eval_file_path.endswith('.xlsx') else pd.read_csv(eval_file_path)
# Show available columns in the evaluation dataset
print("\n--- Available Columns in Evaluation Dataset ---")
print(eval_data.columns)
texts_eval = eval_data[text_column].tolist()
targets_eval = eval_data[target_information_column].tolist()
```
### 3. Define the Task Instruction:
Next, the user must define the task instruction. This defines the type of information that needs to be extracted from the text. Below are some example task scenarios:
#### Example Task Scenarios:
- Extract Authors
- Extract Publication Dates
- Extract Keywords
- Extract Abstract
The task instruction should be chosen based on the user's specific needs. Here's how the user can input their task:
```python
# Define the task instruction
print("\n--- Example Task Scenarios ---")
print("1. Extract Authors")
print("2. Extract Publication Dates")
print("3. Extract Keywords")
print("4. Extract Abstract")
print("Choose the task you want to perform.")
task_instruction = input("Enter your task (e.g., 'Extract authors', 'Extract keywords'): ").strip()
print(f"You chose: {task_instruction}")
```
### 4. Train and Fine-Tune the Model:
Once the dataset is ready, and the task instruction is set, you can train the model by specifying the task and number of epochs.
```python
# Fine-tune the model
num_epochs = int(input("\nHow many epochs would you like to train for? (e.g., 3, 5, 10): "))
print(f"Training for {num_epochs} epoch(s).")
# Initialize the extraction model
from FineTune_Information_Extractor_for_NLPTasks_based_T5_Small import InfoExtractionModel
extractor = InfoExtractionModel()
Ask the user for the model save path
save_model_path = input("\nPlease enter the path where you want to save the trained model (default: './info_extraction_model'): ").strip() or './info_extraction_model'
print(f"Model will be saved at: {save_model_path}")
# Train the model
extractor.train(texts_train, targets_train, texts_eval, targets_eval, task_instruction, num_epochs, save_model_path)
extractor.save_fine_tuned_model(save_model_path)
```
## Load the Trained Model:
After training the model, you can load the trained model for inference or evaluation using the following code:
### 1. Load a Trained Model:
```python
# Load the trained model
extractor.load_model('./info_extraction_model')
print("Model loaded from './info_extraction_model'")
```
### 2. Extract Information from New Text:
You can now use the loaded model to extract information from new text inputs.
The `extract` method accepts the text and task instruction (which was defined earlier, such as "Extract Authors").
```python
# Extract information from new text
new_text='Complete resolution of cutaneous larva migrans with topical ivermectin: A case report Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda Cutaneous larva migrans (CLM; also called creeping eruption) is a cutaneous ectoparasitosis commonly observed in tropical countries. It is characterized by an erythematous, pruritic, and raised lesion with linear or serpiginous distribution, typically localized at the lower extremities. Oral ivermectin represents the most recommended current treatment, with important adverse effects associated. We report the clinical case of a 52‐year old with CLM, successfully treated with topical ivermectin.'
a=extractor.extract(new_text,task_instruction)
```
In this example, `new_text` contains the input text you want the model to process based on the task instruction (e.g., "Extract Authors").
## Clearing Memory:
After completing the evaluation, it's important to clear up memory (especially if you're working with large datasets and models on GPUs). The script uses garbage collection (`gc.collect()`) and CUDA memory clearing (`torch.cuda.empty_cache()`) to free up any allocated memory:
```python
# Clear memory after the process
del texts_train, targets_train, texts_eval, targets_eval, train_data, eval_data
torch.cuda.empty_cache()
gc.collect()
```
### Example output :
```plaintext
Extracting information based on task: Complete resolution of cutaneous larva migrans with topical ivermectin: A case report Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda Cutaneous larva migrans (CLM; also called creeping eruption) is a cutaneous ectoparasitosis commonly observed in tropical countries. It is characterized by an erythematous, pruritic, and raised lesion with linear or serpiginous distribution, typically localized at the lower extremities. Oral ivermectin represents the most recommended current treatment, with important adverse effects associated. We report the clinical case of a 52‐year old with CLM, successfully treated with topical ivermectin.
Extracted information: Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda
```
### Authors
- Mehrdad ALMASI (email: mehrdad.al.2023@gmail.com)
- Demival VASQUES FILHO (email: demival.vasques@uni.lu)
### License
This project is licensed under the MIT License - see the LICENSE file for details.
## Example Usage
This section provides a complete example of how to load a dataset, split it into training and validation sets, fine-tune the `T5-small` model, and evaluate it for information extraction.
### 1. Build and Split the Dataset
If you do not have a dataset, we will build one together using the `leminda-ai/s2orc_small` dataset from Hugging Face. If you already have a dataset, you can skip to the section where you input the training dataset path.
The example below demonstrates how to load the dataset, process it, split it into training and validation sets, and convert it into a Pandas DataFrame.
Make sure the selected columns for training and validation do not contain null values.
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from datasets import load_dataset
# Ask the user if they have a dataset or want to build one
build_dataset = input("Do you want to build a new dataset? (yes/no): ").strip().lower()
if build_dataset == 'yes':
# Step 1: Load the dataset
dataset = load_dataset("leminda-ai/s2orc_small",split="train")
# Convert to a pandas dataframe
# Reduce the size for demonstration purposes
df = pd.DataFrame(dataset[:2000])
print(df.head)
# Step 2: Extract author names from the 'authors' column
def extract_author_names(authors_list):
if authors_list and isinstance(authors_list, list):
return ', '.join([author.get("name", "") for author in authors_list])
return "Unknown"
df['author_names'] = df['authors'].apply(extract_author_names)
# Step 3: Create a new column combining 'title', 'author_names', and 'abstract' (paperAbstract)
df['combined_text'] = df['title'] + " " + df['author_names'] + " " + df['paperAbstract']
df['Authors'] = df['author_names']
# Step 4: Save the updated dataset to CSV
df.to_excel('updated_s2orc_small_with_authors.xlsx', index=False)
print("Dataset updated and saved as 'updated_s2orc_small_with_authors.xlsx'.")
# Step 6: Split the dataset into training and validation sets
data=df
train_data, val_data = train_test_split(data, test_size=0.2, random_state=42)
# Step 7: Save the datasets
train_file_path = input("Please enter the path where the training dataset should be saved (default: ./train_data.xlsx): ").strip() or './train_data.xlsx'
val_file_path = input("Please enter the path where the validation dataset should be saved (default: ./validation_data.xlsx): ").strip() or './validation_data.xlsx'
train_data.to_excel(train_file_path, index=False)
val_data.to_excel(val_file_path, index=False)
print(f"Training dataset saved to: {train_file_path}")
print(f"Validation dataset saved to: {val_file_path}")
else:
# Step 1: Proceed with user inputs for dataset paths if they already have the datasets
train_file_path = input("Please enter the path to the training file (CSV or Excel): ").strip()
val_file_path = input("Please enter the path to the validation file (CSV or Excel): ").strip()
# Step 2: Load the datasets based on their file types
if train_file_path.endswith('.csv'):
train_data = pd.read_csv(train_file_path)
elif train_file_path.endswith('.xlsx'):
train_data = pd.read_excel(train_file_path)
else:
raise ValueError("Unsupported file format. Please provide a CSV or Excel file for the training data.")
if val_file_path.endswith('.csv'):
val_data = pd.read_csv(val_file_path)
elif val_file_path.endswith('.xlsx'):
val_data = pd.read_excel(val_file_path)
else:
raise ValueError("Unsupported file format for validation dataset. Please provide a CSV or Excel file.")
```
### 2. Fine-tune and Evaluate the Model
Once you have your dataset loaded and split, you can fine-tune the `T5-small` model using the following script.
```python
from FineTune_Information_Extractor_for_NLPTasks_based_T5_Small import InfoExtractionModel
# Step 3: Create the model instance
model = InfoExtractionModel()
# Step 4: Load the dataset
train_columns = train_data.columns
val_columns = val_data.columns
# Step 5: Ask the user to choose the columns for training
print(f"Available columns in training dataset: {train_columns}")
text_column = input(f"Please choose the text column from the training dataset (e.g. combined_text): ").strip()
target_column = input(f"Please choose the target column (e.g., 'Authors') from the training dataset: ").strip()
# Step 6: Prepare the data for training
texts_train = train_data[text_column].tolist()
labels_train = train_data[target_column].tolist()
texts_eval = val_data[text_column].tolist()
labels_eval = val_data[target_column].tolist()
# Step 7: User provides task instruction and number of epochs
task_instruction = input("Enter the task instruction (e.g., 'Extract Authors'): ").strip()
num_epochs = int(input("Enter the number of epochs (e.g., 3): "))
# Step 8: Train the model
model.train(texts_train, labels_train, texts_eval, labels_eval, task_instruction, num_epochs)
# Save the fine-tuned model
save_model_path = './info_extraction_model'
model.save_fine_tuned_model(save_model_path)
print(f"Model saved at {save_model_path}")
```
### 3. Extract Information and Evaluate Results
Now that the model is trained, you can extract information from new data and evaluate the results.
```python
# Step 9: Load the trained model
model.load_model('./info_extraction_model')
print("Model loaded from './info_extraction_model'")
# Step 10: Extract information from new text
new_text='Complete resolution of cutaneous larva migrans with topical ivermectin: A case report Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda Cutaneous larva migrans (CLM; also called creeping eruption) is a cutaneous ectoparasitosis commonly observed in tropical countries. It is characterized by an erythematous, pruritic, and raised lesion with linear or serpiginous distribution, typically localized at the lower extremities. Oral ivermectin represents the most recommended current treatment, with important adverse effects associated. We report the clinical case of a 52‐year old with CLM, successfully treated with topical ivermectin.'
a=model.extract(new_text,task_instruction)
```
Raw data
{
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"name": "FineTune-Information-Extractor-for-NLPTasks-based-T5-Small",
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"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "NLP, T5, information extraction, transformers, text generation, fine-tuning",
"author": "Mehrdad ALMASI, Demival VASQUES FILHO",
"author_email": "mehrdad.al.2023@gmail.com, demival.vasques@uni.lu",
"download_url": "https://files.pythonhosted.org/packages/60/a7/19ee5b895392556b42a0b530d9d665309b816ddc1140c0383bbb51466092/FineTune_Information_Extractor_for_NLPTasks_based_T5_Small-1.0.5.tar.gz",
"platform": null,
"description": "# Fine-Tune Information Extractor for NLP Tasks based on T5-Small\n\n[](https://pepy.tech/project/FineTune-Information-Extractor-for-NLPTasks-based-T5-Small)\n\nFine-Tune Information Extractor for NLP Tasks based on T5-Small is a powerful library designed for fine-tuning the pre-trained `t5-small` model on custom information\n extraction tasks. The library provides an intuitive interface for loading datasets, fine-tuning the T5 model, and exporting results efficiently.\n\nIn our [Kaggle](https://www.kaggle.com/code/mehrdadal/finetune-information-extractor-t5-small) example, we fine-tuned the model \nusing the [leminda-ai/s2orc_small](https://huggingface.co/datasets/leminda-ai/s2orc_small) dataset to extract key information, such as author names, \nfrom academic articles. It is important to note that to achieve efficient performance and faster results, \nusers should leverage high-performance GPUs like the **P100** available on Kaggle. In contrast, using GPUs like the **T4** on platforms such as Google Colab may result\n in slower training times and less efficient fine-tuning, which could affect overall output quality for more demanding tasks.\n\n\n## Table of Contents\n- [Key Features](#key-features)\n- [Quick Start](#quick-start)\n- [Fine-tuning the Model](#fine-tuning-the-model)\n- [Model Loading and Inference](#model-loading-and-inference)\n- [Results](#results)\n- [Authors](#authors)\n- [License](#license)\n- [Example Usage](#example-usage)\n\n## Key Features\n- **T5 Fine-tuning**: Fine-tune the `t5-small` model for custom information extraction tasks.\n- **Customizable Task Instructions**: Supports flexible task instructions (e.g., \"Extract Authors\", \"Extract Keywords\").\n- **Text Preprocessing**: Combines and tokenizes multiple text columns for input into the model.\n- **GPU Support**: Utilizes GPU acceleration for faster training.\n\n## Quick Start\nThe primary interface for interacting with this library is the `InfoExtractionModel` class, which allows you to load data, fine-tune the model, and generate output for a given input text.\n\n## Fine-tuning the Model\nTo fine-tune the `t5-small` model, you need to prepare three datasets: one for training, and one for evaluation. These files should be provided in Excel or CSV format. \nThe process involves selecting the correct text column and target information column for information extraction.\n\n### 1. Prepare Train, Evaluation, and Test Files\nYou must provide three separate files for training, evaluation, and testing. Each file should include columns containing the text you want to extract information from (e.g., abstracts or articles) and the specific target information (e.g., authors, dates, keywords) to extract.\n\n- **Train File**: Contains the data to train the model.\n- **Evaluation File**: Contains the data for validating the model during training.\n\n### 2. Load and Show Available Columns\nThe first step is to load the dataset and display the available columns to let the user select the columns for text and target information. \nYou can specify the path to each file, and the code will display the available columns so you can choose which one contains the text and which \none contains the target information.\n\n```python\nimport pandas as pd\n\n# Load the dataset (Excel or CSV)\ntrain_file_path = input(\"Please provide the path for the training dataset file: \")\neval_file_path = input(\"Please provide the path for the evaluation dataset file: \")\n\n# Load the training data to display columns\ntrain_data = pd.read_excel(train_file_path) if train_file_path.endswith('.xlsx') else pd.read_csv(train_file_path)\n\n# Show available columns\nprint(\"\\n--- Available Columns in Training Dataset ---\")\nprint(train_data.columns)\n\n# Ask the user to specify the text and target columns\ntext_column = input(\"\\nEnter the name of the column containing the text data (e.g., 'combined_text'): \").strip()\ntarget_information_column = input(\"Enter the name of the column containing the target information (e.g., 'Authors'): \").strip()\n\n# Extract text and target information from training and evaluation datasets\ntexts_train = train_data[text_column].tolist()\ntargets_train = train_data[target_information_column].tolist()\n\n# Load the evaluation data\neval_data = pd.read_excel(eval_file_path) if eval_file_path.endswith('.xlsx') else pd.read_csv(eval_file_path)\n\n# Show available columns in the evaluation dataset\nprint(\"\\n--- Available Columns in Evaluation Dataset ---\")\nprint(eval_data.columns)\n\ntexts_eval = eval_data[text_column].tolist()\ntargets_eval = eval_data[target_information_column].tolist()\n\n\n```\n### 3. Define the Task Instruction:\nNext, the user must define the task instruction. This defines the type of information that needs to be extracted from the text. Below are some example task scenarios:\n\n#### Example Task Scenarios:\n- Extract Authors\n- Extract Publication Dates\n- Extract Keywords\n- Extract Abstract\n\nThe task instruction should be chosen based on the user's specific needs. Here's how the user can input their task:\n\n```python\n# Define the task instruction\nprint(\"\\n--- Example Task Scenarios ---\")\nprint(\"1. Extract Authors\")\nprint(\"2. Extract Publication Dates\")\nprint(\"3. Extract Keywords\")\nprint(\"4. Extract Abstract\")\nprint(\"Choose the task you want to perform.\")\n\ntask_instruction = input(\"Enter your task (e.g., 'Extract authors', 'Extract keywords'): \").strip()\nprint(f\"You chose: {task_instruction}\")\n\n```\n### 4. Train and Fine-Tune the Model:\nOnce the dataset is ready, and the task instruction is set, you can train the model by specifying the task and number of epochs.\n\n```python\n# Fine-tune the model\nnum_epochs = int(input(\"\\nHow many epochs would you like to train for? (e.g., 3, 5, 10): \"))\nprint(f\"Training for {num_epochs} epoch(s).\")\n\n# Initialize the extraction model\nfrom FineTune_Information_Extractor_for_NLPTasks_based_T5_Small import InfoExtractionModel\nextractor = InfoExtractionModel()\n\n Ask the user for the model save path\nsave_model_path = input(\"\\nPlease enter the path where you want to save the trained model (default: './info_extraction_model'): \").strip() or './info_extraction_model'\nprint(f\"Model will be saved at: {save_model_path}\")\n\n# Train the model\nextractor.train(texts_train, targets_train, texts_eval, targets_eval, task_instruction, num_epochs, save_model_path)\nextractor.save_fine_tuned_model(save_model_path)\n\n```\n## Load the Trained Model:\nAfter training the model, you can load the trained model for inference or evaluation using the following code:\n\n### 1. Load a Trained Model:\n```python\n# Load the trained model\nextractor.load_model('./info_extraction_model')\nprint(\"Model loaded from './info_extraction_model'\")\n\n```\n\n### 2. Extract Information from New Text:\nYou can now use the loaded model to extract information from new text inputs. \nThe `extract` method accepts the text and task instruction (which was defined earlier, such as \"Extract Authors\").\n\n```python\n# Extract information from new text\nnew_text='Complete resolution of cutaneous larva migrans with topical ivermectin: A case report Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda Cutaneous larva migrans (CLM; also called creeping eruption) is a cutaneous ectoparasitosis commonly observed in tropical countries. It is characterized by an erythematous, pruritic, and raised lesion with linear or serpiginous distribution, typically localized at the lower extremities. Oral ivermectin represents the most recommended current treatment, with important adverse effects associated. We report the clinical case of a 52\u2010year old with CLM, successfully treated with topical ivermectin.'\na=extractor.extract(new_text,task_instruction)\n\n```\nIn this example, `new_text` contains the input text you want the model to process based on the task instruction (e.g., \"Extract Authors\").\n\n\n## Clearing Memory:\nAfter completing the evaluation, it's important to clear up memory (especially if you're working with large datasets and models on GPUs). The script uses garbage collection (`gc.collect()`) and CUDA memory clearing (`torch.cuda.empty_cache()`) to free up any allocated memory:\n```python\n# Clear memory after the process\ndel texts_train, targets_train, texts_eval, targets_eval, train_data, eval_data\ntorch.cuda.empty_cache()\ngc.collect()\n```\n### Example output :\n\n```plaintext\nExtracting information based on task: Complete resolution of cutaneous larva migrans with topical ivermectin: A case report Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda Cutaneous larva migrans (CLM; also called creeping eruption) is a cutaneous ectoparasitosis commonly observed in tropical countries. It is characterized by an erythematous, pruritic, and raised lesion with linear or serpiginous distribution, typically localized at the lower extremities. Oral ivermectin represents the most recommended current treatment, with important adverse effects associated. We report the clinical case of a 52\u2010year old with CLM, successfully treated with topical ivermectin.\nExtracted information: Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda\n```\n\n### Authors\n\n- Mehrdad ALMASI (email: mehrdad.al.2023@gmail.com)\n- Demival VASQUES FILHO (email: demival.vasques@uni.lu)\n\n### License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Example Usage\n\nThis section provides a complete example of how to load a dataset, split it into training and validation sets, fine-tune the `T5-small` model, and evaluate it for information extraction.\n\n### 1. Build and Split the Dataset\n\nIf you do not have a dataset, we will build one together using the `leminda-ai/s2orc_small` dataset from Hugging Face. If you already have a dataset, you can skip to the section where you input the training dataset path.\n\nThe example below demonstrates how to load the dataset, process it, split it into training and validation sets, and convert it into a Pandas DataFrame.\n\nMake sure the selected columns for training and validation do not contain null values.\n\n```python\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom datasets import load_dataset\n\n\n# Ask the user if they have a dataset or want to build one\nbuild_dataset = input(\"Do you want to build a new dataset? (yes/no): \").strip().lower()\n\nif build_dataset == 'yes':\n # Step 1: Load the dataset\n dataset = load_dataset(\"leminda-ai/s2orc_small\",split=\"train\")\n\n # Convert to a pandas dataframe\n # Reduce the size for demonstration purposes\n\n df = pd.DataFrame(dataset[:2000])\n print(df.head)\n\n # Step 2: Extract author names from the 'authors' column\n def extract_author_names(authors_list):\n if authors_list and isinstance(authors_list, list):\n return ', '.join([author.get(\"name\", \"\") for author in authors_list])\n return \"Unknown\"\n\n df['author_names'] = df['authors'].apply(extract_author_names)\n\n # Step 3: Create a new column combining 'title', 'author_names', and 'abstract' (paperAbstract)\n df['combined_text'] = df['title'] + \" \" + df['author_names'] + \" \" + df['paperAbstract']\n df['Authors'] = df['author_names']\n\n # Step 4: Save the updated dataset to CSV\n df.to_excel('updated_s2orc_small_with_authors.xlsx', index=False)\n\n print(\"Dataset updated and saved as 'updated_s2orc_small_with_authors.xlsx'.\")\n\n\n # Step 6: Split the dataset into training and validation sets\n data=df\n train_data, val_data = train_test_split(data, test_size=0.2, random_state=42)\n\n # Step 7: Save the datasets\n train_file_path = input(\"Please enter the path where the training dataset should be saved (default: ./train_data.xlsx): \").strip() or './train_data.xlsx'\n val_file_path = input(\"Please enter the path where the validation dataset should be saved (default: ./validation_data.xlsx): \").strip() or './validation_data.xlsx'\n\n train_data.to_excel(train_file_path, index=False)\n val_data.to_excel(val_file_path, index=False)\n\n print(f\"Training dataset saved to: {train_file_path}\")\n print(f\"Validation dataset saved to: {val_file_path}\")\n\nelse:\n # Step 1: Proceed with user inputs for dataset paths if they already have the datasets\n train_file_path = input(\"Please enter the path to the training file (CSV or Excel): \").strip()\n val_file_path = input(\"Please enter the path to the validation file (CSV or Excel): \").strip()\n\n# Step 2: Load the datasets based on their file types\nif train_file_path.endswith('.csv'):\n train_data = pd.read_csv(train_file_path)\nelif train_file_path.endswith('.xlsx'):\n train_data = pd.read_excel(train_file_path)\nelse:\n raise ValueError(\"Unsupported file format. Please provide a CSV or Excel file for the training data.\")\n\nif val_file_path.endswith('.csv'):\n val_data = pd.read_csv(val_file_path)\nelif val_file_path.endswith('.xlsx'):\n val_data = pd.read_excel(val_file_path)\nelse:\n raise ValueError(\"Unsupported file format for validation dataset. Please provide a CSV or Excel file.\")\n```\n### 2. Fine-tune and Evaluate the Model\n\nOnce you have your dataset loaded and split, you can fine-tune the `T5-small` model using the following script.\n\n```python\nfrom FineTune_Information_Extractor_for_NLPTasks_based_T5_Small import InfoExtractionModel\n\n# Step 3: Create the model instance\nmodel = InfoExtractionModel()\n\n# Step 4: Load the dataset\ntrain_columns = train_data.columns\nval_columns = val_data.columns\n\n# Step 5: Ask the user to choose the columns for training\nprint(f\"Available columns in training dataset: {train_columns}\")\ntext_column = input(f\"Please choose the text column from the training dataset (e.g. combined_text): \").strip()\ntarget_column = input(f\"Please choose the target column (e.g., 'Authors') from the training dataset: \").strip()\n\n# Step 6: Prepare the data for training\ntexts_train = train_data[text_column].tolist()\nlabels_train = train_data[target_column].tolist()\n\ntexts_eval = val_data[text_column].tolist()\nlabels_eval = val_data[target_column].tolist()\n\n# Step 7: User provides task instruction and number of epochs\ntask_instruction = input(\"Enter the task instruction (e.g., 'Extract Authors'): \").strip()\nnum_epochs = int(input(\"Enter the number of epochs (e.g., 3): \"))\n\n# Step 8: Train the model\nmodel.train(texts_train, labels_train, texts_eval, labels_eval, task_instruction, num_epochs)\n\n# Save the fine-tuned model\nsave_model_path = './info_extraction_model'\nmodel.save_fine_tuned_model(save_model_path)\nprint(f\"Model saved at {save_model_path}\")\n```\n### 3. Extract Information and Evaluate Results\nNow that the model is trained, you can extract information from new data and evaluate the results.\n\n```python\n# Step 9: Load the trained model\nmodel.load_model('./info_extraction_model')\nprint(\"Model loaded from './info_extraction_model'\")\n\n# Step 10: Extract information from new text\nnew_text='Complete resolution of cutaneous larva migrans with topical ivermectin: A case report Francesca Magri, Camilla Chello, Giulia Pranteda, Guglielmo Pranteda Cutaneous larva migrans (CLM; also called creeping eruption) is a cutaneous ectoparasitosis commonly observed in tropical countries. It is characterized by an erythematous, pruritic, and raised lesion with linear or serpiginous distribution, typically localized at the lower extremities. Oral ivermectin represents the most recommended current treatment, with important adverse effects associated. We report the clinical case of a 52\u2010year old with CLM, successfully treated with topical ivermectin.'\na=model.extract(new_text,task_instruction)\n\n```\n\n",
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