Text Classification
Transformers
Joblib
Safetensors
multilingual
binary-classification
amis
agriculture
Instructions to use faodl/agri-wheat-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faodl/agri-wheat-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faodl/agri-wheat-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("faodl/agri-wheat-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
AMIS Commodity Classifier Training Report
- Dataset:
faodl/amis-agri-wheat - Dataset subset: ``
- Dataset revision:
main - Text column:
chunk_text - Label column:
label - Transformer:
FacebookAI/xlm-roberta-base - Generated at:
2026-05-29T18:13:08.384805+00:00
Dataset Summary
| Split | Rows | Label 0 | Label 1 | Unique groups | Mean text length |
|---|---|---|---|---|---|
| train | 3622 | 2163 | 1459 | 1850 | 644.8 |
| validation | 759 | 486 | 273 | 396 | 636.7 |
| test | 762 | 470 | 292 | 397 | 643.3 |
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 | 0.818 | 0.718 | 0.813 | 0.763 | 0.907 | 0.867 |
| logistic_tfidf | 0.470 | 0.823 | 0.709 | 0.864 | 0.779 | 0.907 | 0.867 |
| xgboost_tfidf | 0.500 | 0.868 | 0.808 | 0.832 | 0.819 | 0.935 | 0.892 |
| xgboost_tfidf | 0.520 | 0.871 | 0.816 | 0.828 | 0.822 | 0.935 | 0.892 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.783 | 0.658 | 0.824 | 0.732 | 0.862 | 0.780 |
| embedding-logistic_sentence_embeddings | 0.521 | 0.791 | 0.673 | 0.813 | 0.736 | 0.862 | 0.780 |
| embedding-svm_sentence_embeddings | 0.500 | 0.804 | 0.714 | 0.758 | 0.735 | 0.869 | 0.792 |
| embedding-svm_sentence_embeddings | 0.473 | 0.805 | 0.704 | 0.791 | 0.745 | 0.869 | 0.792 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.791 | 0.694 | 0.747 | 0.720 | 0.868 | 0.786 |
| embedding-lightgbm_sentence_embeddings | 0.433 | 0.800 | 0.693 | 0.795 | 0.741 | 0.868 | 0.786 |
| transformer | 0.500 | 0.925 | 0.894 | 0.897 | 0.896 | 0.956 | 0.914 |
| transformer | 0.203 | 0.926 | 0.883 | 0.916 | 0.899 | 0.956 | 0.914 |
Threshold Comparison on Test Split
| Model | Threshold | Accuracy | Precision | Recall | F1 | ROC AUC | Average precision |
|---|---|---|---|---|---|---|---|
| logistic_tfidf | 0.500 | 0.803 | 0.715 | 0.808 | 0.759 | 0.888 | 0.827 |
| logistic_tfidf | 0.470 | 0.797 | 0.688 | 0.860 | 0.764 | 0.888 | 0.827 |
| xgboost_tfidf | 0.500 | 0.835 | 0.773 | 0.805 | 0.789 | 0.910 | 0.831 |
| xgboost_tfidf | 0.520 | 0.835 | 0.777 | 0.798 | 0.787 | 0.910 | 0.831 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.782 | 0.699 | 0.757 | 0.727 | 0.877 | 0.821 |
| embedding-logistic_sentence_embeddings | 0.521 | 0.789 | 0.713 | 0.750 | 0.731 | 0.877 | 0.821 |
| embedding-svm_sentence_embeddings | 0.500 | 0.818 | 0.778 | 0.733 | 0.755 | 0.883 | 0.824 |
| embedding-svm_sentence_embeddings | 0.473 | 0.812 | 0.758 | 0.750 | 0.754 | 0.883 | 0.824 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.798 | 0.740 | 0.729 | 0.734 | 0.892 | 0.847 |
| embedding-lightgbm_sentence_embeddings | 0.433 | 0.806 | 0.735 | 0.771 | 0.753 | 0.892 | 0.847 |
| transformer | 0.500 | 0.885 | 0.862 | 0.832 | 0.847 | 0.943 | 0.915 |
| transformer | 0.203 | 0.890 | 0.854 | 0.860 | 0.857 | 0.943 | 0.915 |
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 | 376 | 94 |
| RELEVANT | 56 | 236 |
logistic_tfidf at threshold 0.470
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 356 | 114 |
| RELEVANT | 41 | 251 |
xgboost_tfidf at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 401 | 69 |
| RELEVANT | 57 | 235 |
xgboost_tfidf at threshold 0.520
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 403 | 67 |
| RELEVANT | 59 | 233 |
embedding-logistic_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 375 | 95 |
| RELEVANT | 71 | 221 |
embedding-logistic_sentence_embeddings at threshold 0.521
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 382 | 88 |
| RELEVANT | 73 | 219 |
embedding-svm_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 409 | 61 |
| RELEVANT | 78 | 214 |
embedding-svm_sentence_embeddings at threshold 0.473
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 400 | 70 |
| RELEVANT | 73 | 219 |
embedding-lightgbm_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 395 | 75 |
| RELEVANT | 79 | 213 |
embedding-lightgbm_sentence_embeddings at threshold 0.433
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 389 | 81 |
| RELEVANT | 67 | 225 |
transformer at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 431 | 39 |
| RELEVANT | 49 | 243 |
transformer at threshold 0.203
| True / Predicted | NOT_RELEVANT | RELEVANT |
|---|---|---|
| NOT_RELEVANT | 427 | 43 |
| RELEVANT | 41 | 251 |
Validation-Tuned Thresholds
logistic_tfidf: threshold0.470(validation F10.779); test F1 change vs 0.5:+0.005.xgboost_tfidf: threshold0.520(validation F10.822); test F1 change vs 0.5:-0.001.embedding-logistic_sentence_embeddings: threshold0.521(validation F10.736); test F1 change vs 0.5:+0.004.embedding-svm_sentence_embeddings: threshold0.473(validation F10.745); test F1 change vs 0.5:-0.001.embedding-lightgbm_sentence_embeddings: threshold0.433(validation F10.741); test F1 change vs 0.5:+0.018.transformer: threshold0.203(validation F10.899); test F1 change vs 0.5:+0.010.
Artifacts
logistic_tfidf:/content/agri-wheat-classifier/baselines/logisticxgboost_tfidf:/content/agri-wheat-classifier/baselines/xgboostembedding-logistic_sentence_embeddings:/content/agri-wheat-classifier/baselines/embedding-logisticembedding-svm_sentence_embeddings:/content/agri-wheat-classifier/baselines/embedding-svmembedding-lightgbm_sentence_embeddings:/content/agri-wheat-classifier/baselines/embedding-lightgbmtransformer:/content/agri-wheat-classifier/transformer