Text Classification
Transformers
Joblib
Safetensors
multilingual
binary-classification
amis
agriculture
Instructions to use faodl/agri-utilization-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faodl/agri-utilization-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faodl/agri-utilization-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("faodl/agri-utilization-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 6,531 Bytes
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- Dataset: `faodl/amis-agri-utilization`
- Dataset subset: ``
- Dataset revision: `ada4a04088a98f8f64bc7485c57d4c7f422c2151`
- Text column: `chunk_text`
- Label column: `label`
- Transformer: `FacebookAI/xlm-roberta-base`
- Generated at: `2026-06-10T20:30:54.345579+00:00`
## Dataset Summary
| Split | Rows | Label 0 | Label 1 | Unique groups | Mean text length |
| --- | ---: | ---: | ---: | ---: | ---: |
| train | 4877 | 4347 | 530 | 2513 | 696.6 |
| validation | 978 | 899 | 79 | 538 | 690.6 |
| test | 1016 | 904 | 112 | 539 | 690.7 |
## 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 | 0.912 | 0.465 | 0.582 | 0.517 | 0.872 | 0.594 |
| logistic_tfidf | 0.608 | 0.942 | 0.696 | 0.494 | 0.578 | 0.872 | 0.594 |
| xgboost_tfidf | 0.500 | 0.945 | 0.931 | 0.342 | 0.500 | 0.823 | 0.588 |
| xgboost_tfidf | 0.177 | 0.934 | 0.592 | 0.570 | 0.581 | 0.823 | 0.588 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.912 | 0.476 | 0.861 | 0.613 | 0.953 | 0.762 |
| embedding-logistic_sentence_embeddings | 0.722 | 0.957 | 0.703 | 0.810 | 0.753 | 0.953 | 0.762 |
| embedding-svm_sentence_embeddings | 0.500 | 0.955 | 0.807 | 0.582 | 0.676 | 0.952 | 0.754 |
| embedding-svm_sentence_embeddings | 0.310 | 0.957 | 0.713 | 0.785 | 0.747 | 0.952 | 0.754 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.954 | 0.750 | 0.646 | 0.694 | 0.948 | 0.782 |
| embedding-lightgbm_sentence_embeddings | 0.042 | 0.952 | 0.670 | 0.797 | 0.728 | 0.948 | 0.782 |
| transformer | 0.500 | 0.964 | 0.739 | 0.861 | 0.795 | 0.970 | 0.874 |
| transformer | 0.853 | 0.970 | 0.812 | 0.823 | 0.818 | 0.970 | 0.874 |
## Threshold Comparison on Test Split
| Model | Threshold | Accuracy | Precision | Recall | F1 | ROC AUC | Average precision |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| logistic_tfidf | 0.500 | 0.926 | 0.691 | 0.598 | 0.641 | 0.899 | 0.726 |
| logistic_tfidf | 0.608 | 0.930 | 0.902 | 0.411 | 0.564 | 0.899 | 0.726 |
| xgboost_tfidf | 0.500 | 0.924 | 1.000 | 0.312 | 0.476 | 0.892 | 0.692 |
| xgboost_tfidf | 0.177 | 0.918 | 0.663 | 0.527 | 0.587 | 0.892 | 0.692 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.891 | 0.503 | 0.884 | 0.641 | 0.955 | 0.710 |
| embedding-logistic_sentence_embeddings | 0.722 | 0.935 | 0.689 | 0.750 | 0.718 | 0.955 | 0.710 |
| embedding-svm_sentence_embeddings | 0.500 | 0.930 | 0.741 | 0.562 | 0.640 | 0.956 | 0.704 |
| embedding-svm_sentence_embeddings | 0.310 | 0.934 | 0.686 | 0.741 | 0.712 | 0.956 | 0.704 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.937 | 0.740 | 0.661 | 0.698 | 0.960 | 0.791 |
| embedding-lightgbm_sentence_embeddings | 0.042 | 0.929 | 0.639 | 0.821 | 0.719 | 0.960 | 0.791 |
| transformer | 0.500 | 0.939 | 0.689 | 0.812 | 0.746 | 0.968 | 0.794 |
| transformer | 0.853 | 0.947 | 0.754 | 0.768 | 0.761 | 0.968 | 0.794 |
## 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 | 874 | 30 |
| RELEVANT | 45 | 67 |
### logistic_tfidf at threshold 0.608
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 899 | 5 |
| RELEVANT | 66 | 46 |
### xgboost_tfidf at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 904 | 0 |
| RELEVANT | 77 | 35 |
### xgboost_tfidf at threshold 0.177
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 874 | 30 |
| RELEVANT | 53 | 59 |
### embedding-logistic_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 806 | 98 |
| RELEVANT | 13 | 99 |
### embedding-logistic_sentence_embeddings at threshold 0.722
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 866 | 38 |
| RELEVANT | 28 | 84 |
### embedding-svm_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 882 | 22 |
| RELEVANT | 49 | 63 |
### embedding-svm_sentence_embeddings at threshold 0.310
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 866 | 38 |
| RELEVANT | 29 | 83 |
### embedding-lightgbm_sentence_embeddings at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 878 | 26 |
| RELEVANT | 38 | 74 |
### embedding-lightgbm_sentence_embeddings at threshold 0.042
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 852 | 52 |
| RELEVANT | 20 | 92 |
### transformer at threshold 0.500
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 863 | 41 |
| RELEVANT | 21 | 91 |
### transformer at threshold 0.853
| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 876 | 28 |
| RELEVANT | 26 | 86 |
## Validation-Tuned Thresholds
- `logistic_tfidf`: threshold `0.608` (validation F1 `0.578`); test F1 change vs 0.5: `-0.077`.
- `xgboost_tfidf`: threshold `0.177` (validation F1 `0.581`); test F1 change vs 0.5: `+0.111`.
- `embedding-logistic_sentence_embeddings`: threshold `0.722` (validation F1 `0.753`); test F1 change vs 0.5: `+0.077`.
- `embedding-svm_sentence_embeddings`: threshold `0.310` (validation F1 `0.747`); test F1 change vs 0.5: `+0.073`.
- `embedding-lightgbm_sentence_embeddings`: threshold `0.042` (validation F1 `0.728`); test F1 change vs 0.5: `+0.021`.
- `transformer`: threshold `0.853` (validation F1 `0.818`); test F1 change vs 0.5: `+0.015`.
## Artifacts
- `logistic_tfidf`: `/content/agri-utilization-classifier/baselines/logistic`
- `xgboost_tfidf`: `/content/agri-utilization-classifier/baselines/xgboost`
- `embedding-logistic_sentence_embeddings`: `/content/agri-utilization-classifier/baselines/embedding-logistic`
- `embedding-svm_sentence_embeddings`: `/content/agri-utilization-classifier/baselines/embedding-svm`
- `embedding-lightgbm_sentence_embeddings`: `/content/agri-utilization-classifier/baselines/embedding-lightgbm`
- `transformer`: `/content/agri-utilization-classifier/transformer`
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