Datasets:

ktgiahieu

/

maccrobat2018_2020

like 1

The format is created to be compatible with Huggingface AutoModelForTokenClassification. I didn't create any other format. You can find my example training notebook here:
https://colab.research.google.com/drive/1OzCY782KJSF0FBDS0d1CoMhfp3-RtJMV?usp=sharing

Ah thanks so much i'll play around with it a little.

from datasets import load_dataset
import numpy as np

#But then after that it's back to normal
try:
    # Try to get the feature names directly
    feature_names = MACCROBAT['train'].features['tags'].feature.names
except AttributeError:
    # If 'feature.names' is not available, get the unique tags as before
    all_tags = np.concatenate(MACCROBAT['train']['tags'])
    unique_labels = np.unique(all_tags)
    feature_names = sorted(unique_labels)

# Print sorted labels
print(MACCROBAT)
for i, name in enumerate(feature_names):
    print(f"{i}: {name}")
MACCROBAT_original_labels = {
    0: "B-Activity",
    1: "B-Administration",
    2: "B-Age",
    3: "B-Area",
    4: "B-Biological_attribute",
    5: "B-Biological_structure",
    6: "B-Clinical_event",
    7: "B-Color",
    8: "B-Coreference",
    9: "B-Date",
    10: "B-Detailed_description",
    11: "B-Diagnostic_procedure",
    12: "B-Disease_disorder",
    13: "B-Distance",
    14: "B-Dosage",
    15: "B-Duration",
    16: "B-Family_history",
    17: "B-Frequency",
    18: "B-Height",
    19: "B-History",
    20: "B-Lab_value",
    21: "B-Mass",
    22: "B-Medication",
    23: "B-Nonbiological_location",
    24: "B-Occupation",
    25: "B-Other_entity",
    26: "B-Other_event",
    27: "B-Outcome",
    28: "B-Personal_background",
    29: "B-Qualitative_concept",
    30: "B-Quantitative_concept",
    31: "B-Severity",
    32: "B-Sex",
    33: "B-Shape",
    34: "B-Sign_symptom",
    35: "B-Subject",
    36: "B-Texture",
    37: "B-Therapeutic_procedure",
    38: "B-Time",
    39: "B-Volume",
    40: "B-Weight",
    41: "I-Activity",
    42: "I-Administration",
    43: "I-Age",
    44: "I-Area",
    45: "I-Biological_attribute",
    46: "I-Biological_structure",
    47: "I-Clinical_event",
    48: "I-Color",
    49: "I-Coreference",
    50: "I-Date",
    51: "I-Detailed_description",
    52: "I-Diagnostic_procedure",
    53: "I-Disease_disorder",
    54: "I-Distance",
    55: "I-Dosage",
    56: "I-Duration",
    57: "I-Family_history",
    58: "I-Frequency",
    59: "I-Height",
    60: "I-History",
    61: "I-Lab_value",
    62: "I-Mass",
    63: "I-Medication",
    64: "I-Nonbiological_location",
    65: "I-Occupation",
    66: "I-Other_entity",
    67: "I-Other_event",
    68: "I-Outcome",
    69: "I-Personal_background",
    70: "I-Qualitative_concept",
    71: "I-Quantitative_concept",
    72: "I-Severity",
    73: "I-Shape",
    74: "I-Sign_symptom",
    75: "I-Subject",
    76: "I-Texture",
    77: "I-Therapeutic_procedure",
    78: "I-Time",
    79: "I-Volume",
    80: "I-Weight",
    81: "O",
}

from sklearn.model_selection import train_test_split

# Assume that 'dataset' is a list of dictionaries with 'tokens' and 'tags'
tokens = [example['tokens'] for example in MACCROBAT]
tags = [example['tags'] for example in MACCROBAT]

# Split the dataset into training and testing sets
tokens_train, tokens_test, tags_train, tags_test = train_test_split(tokens, tags, test_size=0.2, random_state=42)

# Convert the split data back into the original format
train_data = [{'tokens': tok, 'tags': tag} for tok, tag in zip(tokens_train, tags_train)]
test_data = [{'tokens': tok, 'tags': tag} for tok, tag in zip(tokens_test, tags_test)]

# Create a dictionary with the split datasets
MACCROBAT = {'train': train_data, 'test': test_data}

This is kind of what I was trying to do, so I can make it a similar format with the others so I can combine them, but I think the problem is that the tokenizer is reading the str label, not the Label_ID. Idk if you have any quick fixes, but I appreciate the reply from earlier I'll check it out.

Thanks again
-Doug

Hi in the original dataset each sample is a text, but here its a list of tokens. How did you split the text into tokens and align the labels? Can you share the code that transformed the original data to this format?
Thanks