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metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
  - news_classification
  - multi_label
datasets:
  - reuters21578
metrics:
  - f1
  - accuracy
model-index:
  - name: distilbert-finetuned-reuters21578-multilabel
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: reuters21578
          type: reuters21578
          config: ModApte
          split: test
          args: ModApte
        metrics:
          - name: F1
            type: f1
            value: 0.8628858578607322
          - name: Accuracy
            type: accuracy
            value: 0.8195625759416768
language:
  - en
pipeline_tag: text-classification
widget:
  - text: >-
      JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of
      International Trade and Industry (MITI) will revise its long-term energy
      supply/demand outlook by August to meet a forecast downtrend in Japanese
      energy demand, ministry officials said.     MITI is expected to lower the
      projection for primary energy supplies in the year 2000 to 550 mln
      kilolitres (kl) from 600 mln, they said.     The decision follows the
      emergence of structural changes in Japanese industry following the rise in
      the value of the yen and a decline in domestic electric power demand.    
      MITI is planning to work out a revised energy supply/demand outlook
      through deliberations of committee meetings of the Agency of Natural
      Resources and Energy, the officials said.     They said MITI will also
      review the breakdown of energy supply sources, including oil, nuclear,
      coal and natural gas.     Nuclear energy provided the bulk of Japan's
      electric power in the fiscal year ended March 31, supplying an estimated
      27 pct on a kilowatt/hour basis, followed by oil (23 pct) and liquefied
      natural gas (21 pct), they noted.  REUTER
    example_title: Example-1

Motivation

Fine-tuning on the Reuters-21578 multilabel dataset is a valuable exercise, especially as it's frequently used in take-home tests during interviews. The dataset's complexity is just right for testing multilabel classification skills within a limited timeframe, while its real-world relevance helps simulate practical challenges. Experimenting with this dataset not only helps candidates prepare for interviews but also hones various skills including preprocessing, feature extraction, and model evaluation.

This model is a fine-tuned version of distilbert-base-cased on the reuters21578 dataset.

Inference Example

from transformers import pipeline

pipe = pipeline("text-classification", model="lxyuan/distilbert-finetuned-reuters21578-multilabel", return_all_scores=True)

# dataset["test"]["text"][2]
news_article = (
    "JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International Trade and "
    "Industry (MITI) will revise its long-term energy supply/demand "
    "outlook by August to meet a forecast downtrend in Japanese "
    "energy demand, ministry officials said. "
    "MITI is expected to lower the projection for primary energy "
    "supplies in the year 2000 to 550 mln kilolitres (kl) from 600 "
    "mln, they said. "
    "The decision follows the emergence of structural changes in "
    "Japanese industry following the rise in the value of the yen "
    "and a decline in domestic electric power demand. "
    "MITI is planning to work out a revised energy supply/demand "
    "outlook through deliberations of committee meetings of the "
    "Agency of Natural Resources and Energy, the officials said. "
    "They said MITI will also review the breakdown of energy "
    "supply sources, including oil, nuclear, coal and natural gas. "
    "Nuclear energy provided the bulk of Japan's electric power "
    "in the fiscal year ended March 31, supplying an estimated 27 "
    "pct on a kilowatt/hour basis, followed by oil (23 pct) and "
    "liquefied natural gas (21 pct), they noted. "
    "REUTER"
)

# dataset["test"]["topics"][2]
target_topics = ['crude', 'nat-gas']

fn_kwargs={"padding": "max_length", "truncation": True, "max_length": 512}
output = pipe(example, function_to_apply="sigmoid", **fn_kwargs)

for item in output[0]:
    if item["score"]>=0.5:
        print(item["label"], item["score"])

>>> crude 0.7355073690414429
nat-gas 0.8600426316261292

Overall Summary and Comparison Table

Metric Baseline (Scikit-learn) Transformer Model
Micro-Averaged F1 0.77 0.86
Macro-Averaged F1 0.29 0.33
Weighted Average F1 0.70 0.84
Samples Average F1 0.75 0.80

Precision vs Recall: Both models prioritize high precision over recall. In our client-facing news classification model, precision takes precedence over recall. This is because the repercussions of false positives are more severe and harder to justify to clients compared to false negatives. When the model incorrectly tags a news item with a topic, it's challenging to explain this error. On the other hand, if the model misses a topic, it's easier to defend by stating that the topic wasn't sufficiently emphasized in the news article.

Class Imbalance Handling: Both models suffer from the same general issue of not performing well on minority classes, as reflected in the low macro-averaged F1-scores. However, the transformer model shows a slight improvement, albeit marginal, in macro-averaged F1-score (0.33 vs 0.29).

Issue of Zero Support Labels: Both models have the problem of zero support for several labels, meaning these labels did not appear in the test set. This lack of "support" can significantly skew the performance metrics and may suggest that either the models are not well-tuned to predict these minority classes, or the dataset itself lacks sufficient examples of these classes. Given that both models struggle with low macro-averaged F1 scores, this issue further emphasizes the need for improved minority class handling in the models.

General Performance: The transformer model surpasses the scikit-learn baseline in terms of weighted and samples average F1-scores, indicating better overall performance and better handling of label imbalance.

Conclusion: While both models exhibit high precision, which is a business requirement, the transformer model slightly outperforms the scikit-learn baseline model in all metrics considered. It provides a better trade-off between precision and recall, as well as some improvement, albeit small, in handling minority classes. Thus, despite sharing similar weaknesses with the baseline, the transformer model demonstrates incremental improvements that could be significant in a production setting.

Training and evaluation data

We remove single appearance label from both training and test sets using the following code:

# Find Single Appearance Labels
def find_single_appearance_labels(y):
    """Find labels that appear only once in the dataset."""
    all_labels = list(chain.from_iterable(y))
    label_count = Counter(all_labels)
    single_appearance_labels = [label for label, count in label_count.items() if count == 1]
    return single_appearance_labels

# Remove Single Appearance Labels from Dataset
def remove_single_appearance_labels(dataset, single_appearance_labels):
    """Remove samples with single-appearance labels from both train and test sets."""
    for split in ['train', 'test']:
        dataset[split] = dataset[split].filter(lambda x: all(label not in single_appearance_labels for label in x['topics']))
    return dataset

dataset = load_dataset("reuters21578", "ModApte")

# Find and Remove Single Appearance Labels
y_train = [item['topics'] for item in dataset['train']]
single_appearance_labels = find_single_appearance_labels(y_train)
print(f"Single appearance labels: {single_appearance_labels}")
>>> Single appearance labels: ['lin-oil', 'rye', 'red-bean', 'groundnut-oil', 'citruspulp', 'rape-meal', 'corn-oil', 'peseta', 'cotton-oil', 'ringgit', 'castorseed', 'castor-oil', 'lit', 'rupiah', 'skr', 'nkr', 'dkr', 'sun-meal', 'lin-meal', 'cruzado']

print("Removing samples with single-appearance labels...")
dataset = remove_single_appearance_labels(dataset, single_appearance_labels)

unique_labels = set(chain.from_iterable(dataset['train']["topics"]))
print(f"We have {len(unique_labels)} unique labels:\n{unique_labels}")
>>> We have 95 unique labels:
{'veg-oil', 'gold', 'platinum', 'ipi', 'acq', 'carcass', 'wool', 'coconut-oil', 'linseed', 'copper', 'soy-meal', 'jet', 'dlr', 'copra-cake', 'hog', 'rand', 'strategic-metal', 'can', 'tea', 'sorghum', 'livestock', 'barley', 'lumber', 'earn', 'wheat', 'trade', 'soy-oil', 'cocoa', 'inventories', 'income', 'rubber', 'tin', 'iron-steel', 'ship', 'rapeseed', 'wpi', 'sun-oil', 'pet-chem', 'palmkernel', 'nat-gas', 'gnp', 'l-cattle', 'propane', 'rice', 'lead', 'alum', 'instal-debt', 'saudriyal', 'cpu', 'jobs', 'meal-feed', 'oilseed', 'dmk', 'plywood', 'zinc', 'retail', 'dfl', 'cpi', 'crude', 'pork-belly', 'gas', 'money-fx', 'corn', 'tapioca', 'palladium', 'lei', 'cornglutenfeed', 'sunseed', 'potato', 'silver', 'sugar', 'grain', 'groundnut', 'naphtha', 'orange', 'soybean', 'coconut', 'stg', 'cotton', 'yen', 'rape-oil', 'palm-oil', 'oat', 'reserves', 'housing', 'interest', 'coffee', 'fuel', 'austdlr', 'money-supply', 'heat', 'fishmeal', 'bop', 'nickel', 'nzdlr'}

Training procedure

EDA on Reuters-21578 dataset: This notebook provides an Exploratory Data Analysis (EDA) of the Reuters-21578 dataset. It includes visualizations and statistical summaries that offer insights into the dataset's structure, label distribution, and text characteristics.

Reuters Baseline Scikit-Learn Model: This notebook establishes a baseline model for text classification on the Reuters-21578 dataset using scikit-learn. It guides you through data preprocessing, feature extraction, model training, and evaluation.

Reuters Transformer Model: This notebook delves into advanced text classification using a Transformer model on the Reuters-21578 dataset. It covers the implementation details, training process, and performance metrics of using Transformer-based models for this specific task.

Multilabel Stratified Sampling & Hypyerparameter Search on Reuters Dataset: In this notebook, we explore advanced machine learning techniques through the lens of the Hugging Face Trainer API, specifically targeting Multilabel Iterative Stratified Splitting and Hyperparameter Search. The former aims to fairly distribute imbalanced datasets across multiple labels in k-fold cross-validation, maintaining a distribution closely resembling that of the complete dataset. The latter walks users through a structured hyperparameter search to fine-tune model performance for optimal results.

Evaluation results

Transformer Model Evaluation Result

Classification Report: precision recall f1-score support

        acq       0.97      0.93      0.95       719
       alum       1.00      0.70      0.82        23
    austdlr       0.00      0.00      0.00         0
     barley       1.00      0.50      0.67        12
        bop       0.79      0.50      0.61        30
        can       0.00      0.00      0.00         0
    carcass       0.67      0.67      0.67        18
      cocoa       1.00      1.00      1.00        18
    coconut       0.00      0.00      0.00         2
coconut-oil       0.00      0.00      0.00         2
     coffee       0.86      0.89      0.87        27
     copper       1.00      0.78      0.88        18
 copra-cake       0.00      0.00      0.00         1
       corn       0.84      0.87      0.86        55
cornglutenfeed       0.00      0.00      0.00         0
     cotton       0.92      0.67      0.77        18
        cpi       0.86      0.43      0.57        28
        cpu       0.00      0.00      0.00         1
      crude       0.87      0.93      0.90       189
        dfl       0.00      0.00      0.00         1
        dlr       0.72      0.64      0.67        44
        dmk       0.00      0.00      0.00         4
       earn       0.98      0.99      0.98      1087
   fishmeal       0.00      0.00      0.00         0
       fuel       0.00      0.00      0.00        10
        gas       0.80      0.71      0.75        17
        gnp       0.79      0.66      0.72        35
       gold       0.95      0.67      0.78        30
      grain       0.94      0.92      0.93       146
  groundnut       0.00      0.00      0.00         4
       heat       0.00      0.00      0.00         5
        hog       1.00      0.33      0.50         6
    housing       0.00      0.00      0.00         4
     income       0.00      0.00      0.00         7
instal-debt       0.00      0.00      0.00         1
   interest       0.89      0.67      0.77       131
inventories       0.00      0.00      0.00         0
        ipi       1.00      0.58      0.74        12
 iron-steel       0.90      0.64      0.75        14
        jet       0.00      0.00      0.00         1
       jobs       0.92      0.57      0.71        21
   l-cattle       0.00      0.00      0.00         2
       lead       0.00      0.00      0.00        14
        lei       0.00      0.00      0.00         3
    linseed       0.00      0.00      0.00         0
  livestock       0.63      0.79      0.70        24
     lumber       0.00      0.00      0.00         6
  meal-feed       0.00      0.00      0.00        17
   money-fx       0.78      0.81      0.80       177
   money-supply       0.80      0.71      0.75        34
    naphtha       0.00      0.00      0.00         4
    nat-gas       0.82      0.60      0.69        30
     nickel       0.00      0.00      0.00         1
      nzdlr       0.00      0.00      0.00         2
        oat       0.00      0.00      0.00         4
    oilseed       0.64      0.61      0.63        44
     orange       1.00      0.36      0.53        11
  palladium       0.00      0.00      0.00         1
   palm-oil       1.00      0.56      0.71         9
 palmkernel       0.00      0.00      0.00         1
   pet-chem       0.00      0.00      0.00        12
   platinum       0.00      0.00      0.00         7
    plywood       0.00      0.00      0.00         0
 pork-belly       0.00      0.00      0.00         0
     potato       0.00      0.00      0.00         3
    propane       0.00      0.00      0.00         3
       rand       0.00      0.00      0.00         1
   rape-oil       0.00      0.00      0.00         1
   rapeseed       0.00      0.00      0.00         8
   reserves       0.83      0.56      0.67        18
     retail       0.00      0.00      0.00         2
       rice       1.00      0.57      0.72        23
     rubber       0.82      0.75      0.78        12
  saudriyal       0.00      0.00      0.00         0
       ship       0.95      0.81      0.87        89
     silver       1.00      0.12      0.22         8
    sorghum       1.00      0.12      0.22         8
   soy-meal       0.00      0.00      0.00        12
    soy-oil       0.00      0.00      0.00         8
    soybean       0.72      0.56      0.63        32
        stg       0.00      0.00      0.00         0
  strategic-metal       0.00      0.00      0.00        11
      sugar       1.00      0.80      0.89        35
    sun-oil       0.00      0.00      0.00         0
    sunseed       0.00      0.00      0.00         5
    tapioca       0.00      0.00      0.00         0
        tea       0.00      0.00      0.00         3
        tin       1.00      0.42      0.59        12
      trade       0.78      0.79      0.79       116
    veg-oil       0.91      0.59      0.71        34
      wheat       0.83      0.83      0.83        69
       wool       0.00      0.00      0.00         0
        wpi       0.00      0.00      0.00        10
        yen       0.57      0.29      0.38        14
       zinc       1.00      0.69      0.82        13

  micro avg       0.92      0.81      0.86      3694
  macro avg       0.41      0.30      0.33      3694

weighted avg 0.87 0.81 0.84 3694 samples avg 0.81 0.80 0.80 3694

Scikit-learn Baseline Model Evaluation Result Classification Report: precision recall f1-score support
        acq       0.98      0.87      0.92       719
       alum       1.00      0.00      0.00        23
    austdlr       1.00      1.00      1.00         0
     barley       1.00      0.00      0.00        12
        bop       1.00      0.30      0.46        30
        can       1.00      1.00      1.00         0
    carcass       1.00      0.06      0.11        18
      cocoa       1.00      0.61      0.76        18
    coconut       1.00      0.00      0.00         2
coconut-oil       1.00      0.00      0.00         2
     coffee       0.94      0.59      0.73        27
     copper       1.00      0.22      0.36        18
 copra-cake       1.00      0.00      0.00         1
       corn       0.97      0.51      0.67        55
 cornglutenfeed       1.00      1.00      1.00         0
     cotton       1.00      0.06      0.11        18
        cpi       1.00      0.14      0.25        28
        cpu       1.00      0.00      0.00         1
      crude       0.94      0.69      0.80       189
        dfl       1.00      0.00      0.00         1
        dlr       0.86      0.43      0.58        44
        dmk       1.00      0.00      0.00         4
       earn       0.99      0.97      0.98      1087
   fishmeal       1.00      1.00      1.00         0
       fuel       1.00      0.00      0.00        10
        gas       1.00      0.00      0.00        17
        gnp       1.00      0.31      0.48        35
       gold       0.83      0.17      0.28        30
      grain       1.00      0.65      0.79       146
  groundnut       1.00      0.00      0.00         4
       heat       1.00      0.00      0.00         5
        hog       1.00      0.00      0.00         6
    housing       1.00      0.00      0.00         4
     income       1.00      0.00      0.00         7
instal-debt       1.00      0.00      0.00         1
   interest       0.88      0.40      0.55       131
inventories       1.00      1.00      1.00         0
        ipi       1.00      0.00      0.00        12
 iron-steel       1.00      0.00      0.00        14
        jet       1.00      0.00      0.00         1
       jobs       1.00      0.14      0.25        21
   l-cattle       1.00      0.00      0.00         2
       lead       1.00      0.00      0.00        14
        lei       1.00      0.00      0.00         3
    linseed       1.00      1.00      1.00         0
  livestock       0.67      0.08      0.15        24
     lumber       1.00      0.00      0.00         6
  meal-feed       1.00      0.00      0.00        17
   money-fx       0.80      0.50      0.62       177
 money-supply       0.88      0.41      0.56        34
    naphtha       1.00      0.00      0.00         4
    nat-gas       1.00      0.27      0.42        30
     nickel       1.00      0.00      0.00         1
      nzdlr       1.00      0.00      0.00         2
        oat       1.00      0.00      0.00         4
    oilseed       0.62      0.11      0.19        44
     orange       1.00      0.00      0.00        11
  palladium       1.00      0.00      0.00         1
   palm-oil       1.00      0.22      0.36         9
 palmkernel       1.00      0.00      0.00         1
   pet-chem       1.00      0.00      0.00        12
   platinum       1.00      0.00      0.00         7
    plywood       1.00      1.00      1.00         0
 pork-belly       1.00      1.00      1.00         0
     potato       1.00      0.00      0.00         3
    propane       1.00      0.00      0.00         3
       rand       1.00      0.00      0.00         1
   rape-oil       1.00      0.00      0.00         1
   rapeseed       1.00      0.00      0.00         8
   reserves       1.00      0.00      0.00        18
     retail       1.00      0.00      0.00         2
       rice       1.00      0.00      0.00        23
     rubber       1.00      0.17      0.29        12
  saudriyal       1.00      1.00      1.00         0
       ship       0.92      0.26      0.40        89
     silver       1.00      0.00      0.00         8
    sorghum       1.00      0.00      0.00         8
   soy-meal       1.00      0.00      0.00        12
    soy-oil       1.00      0.00      0.00         8
    soybean       1.00      0.16      0.27        32
        stg       1.00      1.00      1.00         0
  strategic-metal       1.00      0.00      0.00        11
      sugar       1.00      0.60      0.75        35
    sun-oil       1.00      1.00      1.00         0
    sunseed       1.00      0.00      0.00         5
    tapioca       1.00      1.00      1.00         0
        tea       1.00      0.00      0.00         3
        tin       1.00      0.00      0.00        12
      trade       0.92      0.61      0.74       116
    veg-oil       1.00      0.12      0.21        34
      wheat       0.97      0.55      0.70        69
       wool       1.00      1.00      1.00         0
        wpi       1.00      0.00      0.00        10
        yen       1.00      0.00      0.00        14
       zinc       1.00      0.00      0.00        13

  micro avg       0.97      0.64      0.77      3694
  macro avg       0.98      0.25      0.29      3694

weighted avg 0.96 0.64 0.70 3694 samples avg 0.98 0.74 0.75 3694

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
0.1801 1.0 300 0.0439 0.3896 0.6210 0.3566
0.0345 2.0 600 0.0287 0.6289 0.7318 0.5954
0.0243 3.0 900 0.0219 0.6721 0.7579 0.6084
0.0178 4.0 1200 0.0177 0.7505 0.8128 0.6908
0.014 5.0 1500 0.0151 0.7905 0.8376 0.7278
0.0115 6.0 1800 0.0135 0.8132 0.8589 0.7555
0.0096 7.0 2100 0.0124 0.8291 0.8727 0.7725
0.0082 8.0 2400 0.0124 0.8335 0.8757 0.7822
0.0071 9.0 2700 0.0119 0.8392 0.8847 0.7883
0.0064 10.0 3000 0.0123 0.8339 0.8810 0.7828
0.0058 11.0 3300 0.0114 0.8538 0.8999 0.8047
0.0053 12.0 3600 0.0113 0.8525 0.8967 0.8044
0.0048 13.0 3900 0.0115 0.8520 0.8982 0.8029
0.0045 14.0 4200 0.0111 0.8566 0.8962 0.8104
0.0042 15.0 4500 0.0110 0.8610 0.9060 0.8165
0.0039 16.0 4800 0.0112 0.8583 0.9021 0.8138
0.0037 17.0 5100 0.0110 0.8620 0.9055 0.8196
0.0035 18.0 5400 0.0110 0.8629 0.9063 0.8196
0.0035 19.0 5700 0.0111 0.8624 0.9062 0.8180
0.0034 20.0 6000 0.0111 0.8626 0.9055 0.8177

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.3
  • Tokenizers 0.13.3