Text Classification
Transformers
Joblib
Safetensors
multilingual
binary-classification
amis
agriculture
Instructions to use faodl/agri-maize_corn-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faodl/agri-maize_corn-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faodl/agri-maize_corn-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("faodl/agri-maize_corn-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
AMIS Commodity Classifier Training Report
- Dataset:
faodl/amis-agri-maize_corn - Dataset subset: ``
- Dataset revision:
main - Text column:
chunk_text - Label column:
label - Transformer:
FacebookAI/xlm-roberta-base - Generated at:
2026-06-05T20:32:51.106165+00:00
Dataset Summary
| Split | Rows | Label 0 | Label 1 | Unique groups | Mean text length |
|---|---|---|---|---|---|
| train | 4724 | 3822 | 902 | 2226 | 702.9 |
| validation | 1060 | 843 | 217 | 477 | 708.3 |
| test | 1054 | 819 | 235 | 478 | 711.9 |
Threshold Comparison on Validation Split
Validation metrics document threshold selection and tuning behavior; test metrics remain the primary estimate of out-of-sample performance.
| Model | Threshold | Accuracy | Precision | Recall | F1 | ROC AUC | Average precision |
|---|---|---|---|---|---|---|---|
| logistic_tfidf | 0.500 | 0.896 | 0.700 | 0.862 | 0.773 | 0.929 | 0.841 |
| logistic_tfidf | 0.586 | 0.915 | 0.777 | 0.820 | 0.798 | 0.929 | 0.841 |
| xgboost_tfidf | 0.500 | 0.957 | 0.913 | 0.871 | 0.892 | 0.967 | 0.914 |
| xgboost_tfidf | 0.379 | 0.958 | 0.902 | 0.894 | 0.898 | 0.967 | 0.914 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.881 | 0.649 | 0.912 | 0.759 | 0.959 | 0.867 |
| embedding-logistic_sentence_embeddings | 0.744 | 0.923 | 0.808 | 0.816 | 0.812 | 0.959 | 0.867 |
| embedding-svm_sentence_embeddings | 0.500 | 0.913 | 0.849 | 0.700 | 0.768 | 0.955 | 0.858 |
| embedding-svm_sentence_embeddings | 0.401 | 0.914 | 0.789 | 0.793 | 0.791 | 0.955 | 0.858 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.916 | 0.791 | 0.802 | 0.796 | 0.963 | 0.878 |
| embedding-lightgbm_sentence_embeddings | 0.145 | 0.916 | 0.746 | 0.894 | 0.813 | 0.963 | 0.878 |
| transformer | 0.500 | 0.958 | 0.913 | 0.876 | 0.894 | 0.973 | 0.943 |
| transformer | 0.328 | 0.959 | 0.907 | 0.894 | 0.900 | 0.973 | 0.943 |
Threshold Comparison on Test Split
| Model | Threshold | Accuracy | Precision | Recall | F1 | ROC AUC | Average precision |
|---|---|---|---|---|---|---|---|
| logistic_tfidf | 0.500 | 0.910 | 0.787 | 0.817 | 0.802 | 0.953 | 0.863 |
| logistic_tfidf | 0.586 | 0.915 | 0.857 | 0.740 | 0.795 | 0.953 | 0.863 |
| xgboost_tfidf | 0.500 | 0.942 | 0.914 | 0.817 | 0.863 | 0.968 | 0.920 |
| xgboost_tfidf | 0.379 | 0.948 | 0.895 | 0.868 | 0.881 | 0.968 | 0.920 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.884 | 0.704 | 0.830 | 0.762 | 0.936 | 0.844 |
| embedding-logistic_sentence_embeddings | 0.744 | 0.896 | 0.831 | 0.668 | 0.741 | 0.936 | 0.844 |
| embedding-svm_sentence_embeddings | 0.500 | 0.894 | 0.892 | 0.596 | 0.714 | 0.932 | 0.842 |
| embedding-svm_sentence_embeddings | 0.401 | 0.902 | 0.851 | 0.681 | 0.757 | 0.932 | 0.842 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.907 | 0.870 | 0.685 | 0.767 | 0.953 | 0.873 |
| embedding-lightgbm_sentence_embeddings | 0.145 | 0.901 | 0.784 | 0.770 | 0.777 | 0.953 | 0.873 |
| transformer | 0.500 | 0.935 | 0.892 | 0.804 | 0.846 | 0.953 | 0.890 |
| transformer | 0.328 | 0.935 | 0.881 | 0.817 | 0.848 | 0.953 | 0.890 |
Confusion Matrices on Test Split
Rows are true labels and columns are predicted labels.
logistic_tfidf at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 767 | 52 |
| RELEVANT | 43 | 192 |
logistic_tfidf at threshold 0.586
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 790 | 29 |
| RELEVANT | 61 | 174 |
xgboost_tfidf at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 801 | 18 |
| RELEVANT | 43 | 192 |
xgboost_tfidf at threshold 0.379
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 795 | 24 |
| RELEVANT | 31 | 204 |
embedding-logistic_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 737 | 82 |
| RELEVANT | 40 | 195 |
embedding-logistic_sentence_embeddings at threshold 0.744
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 787 | 32 |
| RELEVANT | 78 | 157 |
embedding-svm_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 802 | 17 |
| RELEVANT | 95 | 140 |
embedding-svm_sentence_embeddings at threshold 0.401
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 791 | 28 |
| RELEVANT | 75 | 160 |
embedding-lightgbm_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 795 | 24 |
| RELEVANT | 74 | 161 |
embedding-lightgbm_sentence_embeddings at threshold 0.145
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 769 | 50 |
| RELEVANT | 54 | 181 |
transformer at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 796 | 23 |
| RELEVANT | 46 | 189 |
transformer at threshold 0.328
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 793 | 26 |
| RELEVANT | 43 | 192 |
Validation-Tuned Thresholds
logistic_tfidf: threshold0.586(validation F10.798); test F1 change vs 0.5:-0.007.xgboost_tfidf: threshold0.379(validation F10.898); test F1 change vs 0.5:+0.018.embedding-logistic_sentence_embeddings: threshold0.744(validation F10.812); test F1 change vs 0.5:-0.021.embedding-svm_sentence_embeddings: threshold0.401(validation F10.791); test F1 change vs 0.5:+0.042.embedding-lightgbm_sentence_embeddings: threshold0.145(validation F10.813); test F1 change vs 0.5:+0.010.transformer: threshold0.328(validation F10.900); test F1 change vs 0.5:+0.002.
Artifacts
logistic_tfidf:/content/agri-maize_corn-classifier/baselines/logisticxgboost_tfidf:/content/agri-maize_corn-classifier/baselines/xgboostembedding-logistic_sentence_embeddings:/content/agri-maize_corn-classifier/baselines/embedding-logisticembedding-svm_sentence_embeddings:/content/agri-maize_corn-classifier/baselines/embedding-svmembedding-lightgbm_sentence_embeddings:/content/agri-maize_corn-classifier/baselines/embedding-lightgbmtransformer:/content/agri-maize_corn-classifier/transformer