README.md

---
library_name: transformers
pipeline_tag: text-classification
base_model: FacebookAI/xlm-roberta-base
tags:
- text-classification
- binary-classification
- amis
- agriculture
language: multilingual
---

# AMIS Commodity Classifier

This model repository contains artifacts from an AMIS commodity relevance classifier training run.
It includes the Transformer model, any configured TF-IDF or sentence-embedding baselines, prediction files, and the training report.

- Dataset: `faodl/amis-agri-trade-pri-sec`
- Dataset subset: ``
- Text column: `chunk_text`
- Label column: `label`
- Transformer: `FacebookAI/xlm-roberta-base`
- Generated at: `2026-05-18T17:47:01.228362+00:00`

## Dataset Summary

| Split | Rows | Label 0 | Label 1 | Unique groups | Mean text length |
| --- | ---: | ---: | ---: | ---: | ---: |
| train | 4799 | 2363 | 2436 | 2483 | 695.5 |
| validation | 1009 | 462 | 547 | 532 | 698.1 |
| test | 1017 | 529 | 488 | 533 | 694.6 |

## Threshold Comparison on Test Split

| Model | Threshold | Accuracy | Precision | Recall | F1 | ROC AUC | Average precision |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| logistic_tfidf | 0.500 | 0.738 | 0.736 | 0.709 | 0.722 | 0.838 | 0.815 |
| logistic_tfidf | 0.396 | 0.744 | 0.674 | 0.904 | 0.772 | 0.838 | 0.815 |
| xgboost_tfidf | 0.500 | 0.762 | 0.786 | 0.693 | 0.736 | 0.847 | 0.816 |
| xgboost_tfidf | 0.305 | 0.752 | 0.685 | 0.895 | 0.776 | 0.847 | 0.816 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.790 | 0.750 | 0.842 | 0.793 | 0.881 | 0.863 |
| embedding-logistic_sentence_embeddings | 0.315 | 0.771 | 0.698 | 0.922 | 0.794 | 0.881 | 0.863 |
| embedding-svm_sentence_embeddings | 0.500 | 0.788 | 0.742 | 0.855 | 0.794 | 0.883 | 0.865 |
| embedding-svm_sentence_embeddings | 0.453 | 0.796 | 0.735 | 0.900 | 0.809 | 0.883 | 0.865 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.782 | 0.744 | 0.832 | 0.785 | 0.880 | 0.867 |
| embedding-lightgbm_sentence_embeddings | 0.148 | 0.759 | 0.685 | 0.922 | 0.786 | 0.880 | 0.867 |
| transformer | 0.500 | 0.837 | 0.786 | 0.906 | 0.842 | 0.919 | 0.913 |
| transformer | 0.383 | 0.837 | 0.771 | 0.939 | 0.847 | 0.919 | 0.913 |

## 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 | 405 | 124 |
| RELEVANT | 142 | 346 |

### logistic_tfidf at threshold 0.396

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 316 | 213 |
| RELEVANT | 47 | 441 |

### xgboost_tfidf at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 437 | 92 |
| RELEVANT | 150 | 338 |

### xgboost_tfidf at threshold 0.305

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 328 | 201 |
| RELEVANT | 51 | 437 |

### embedding-logistic_sentence_embeddings at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 392 | 137 |
| RELEVANT | 77 | 411 |

### embedding-logistic_sentence_embeddings at threshold 0.315

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 334 | 195 |
| RELEVANT | 38 | 450 |

### embedding-svm_sentence_embeddings at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 384 | 145 |
| RELEVANT | 71 | 417 |

### embedding-svm_sentence_embeddings at threshold 0.453

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 371 | 158 |
| RELEVANT | 49 | 439 |

### embedding-lightgbm_sentence_embeddings at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 389 | 140 |
| RELEVANT | 82 | 406 |

### embedding-lightgbm_sentence_embeddings at threshold 0.148

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 322 | 207 |
| RELEVANT | 38 | 450 |

### transformer at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 409 | 120 |
| RELEVANT | 46 | 442 |

### transformer at threshold 0.383

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 393 | 136 |
| RELEVANT | 30 | 458 |


## Validation-Tuned Thresholds

- `logistic_tfidf`: threshold `0.396` (validation F1 `0.811`); test F1 change vs 0.5: `+0.050`.
- `xgboost_tfidf`: threshold `0.305` (validation F1 `0.813`); test F1 change vs 0.5: `+0.040`.
- `embedding-logistic_sentence_embeddings`: threshold `0.315` (validation F1 `0.859`); test F1 change vs 0.5: `+0.001`.
- `embedding-svm_sentence_embeddings`: threshold `0.453` (validation F1 `0.861`); test F1 change vs 0.5: `+0.015`.
- `embedding-lightgbm_sentence_embeddings`: threshold `0.148` (validation F1 `0.866`); test F1 change vs 0.5: `+0.001`.
- `transformer`: threshold `0.383` (validation F1 `0.874`); test F1 change vs 0.5: `+0.005`.

## Artifacts

- `logistic_tfidf`: `/content/agri-trade-classifier/baselines/logistic`
- `xgboost_tfidf`: `/content/agri-trade-classifier/baselines/xgboost`
- `embedding-logistic_sentence_embeddings`: `/content/agri-trade-classifier/baselines/embedding-logistic`
- `embedding-svm_sentence_embeddings`: `/content/agri-trade-classifier/baselines/embedding-svm`
- `embedding-lightgbm_sentence_embeddings`: `/content/agri-trade-classifier/baselines/embedding-lightgbm`
- `transformer`: `/content/agri-trade-classifier/transformer`

## Inference

Install the runtime dependencies:

```bash
pip install transformers torch huggingface_hub pandas joblib scikit-learn xgboost sentence-transformers lightgbm
```

### Transformer

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL_ID = "faodl/agri-trade-classifier"

texts = [
    "Rice export prices increased after new procurement rules were announced.",
    "The finance ministry released its monthly fuel tax bulletin.",
]

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, subfolder="transformer")
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, subfolder="transformer")
threshold = float(getattr(model.config, "threshold", 0.5))

encoded = tokenizer(
    texts,
    truncation=True,
    padding=True,
    max_length=256,
    return_tensors="pt",
)

with torch.no_grad():
    logits = model(**encoded).logits
    probabilities = torch.softmax(logits, dim=-1)[:, 1].tolist()

for text, probability in zip(texts, probabilities):
    label = model.config.id2label[int(probability >= threshold)]
    print({"text": text, "probability_positive": probability, "label": label})
```

### TF-IDF Baselines

Available baseline names in this run: "logistic", "xgboost".

```python
import json
import joblib
from huggingface_hub import hf_hub_download

MODEL_ID = "faodl/agri-trade-classifier"
BASELINE = "logistic"

texts = [
    "Maize production forecasts were revised after delayed rains.",
    "The central bank published new exchange rate statistics.",
]

model_path = hf_hub_download(
    repo_id=MODEL_ID,
    repo_type="model",
    filename=f"baselines/{BASELINE}/{BASELINE}_tfidf.joblib",
)
report_path = hf_hub_download(
    repo_id=MODEL_ID,
    repo_type="model",
    filename="report.json",
)

pipeline = joblib.load(model_path)
with open(report_path, encoding="utf-8") as handle:
    report = json.load(handle)

threshold = next(
    result["validation_best_threshold"]["threshold"]
    for result in report["results"]
    if result["model_type"] == f"{BASELINE}_tfidf"
)

probabilities = pipeline.predict_proba(texts)[:, 1]
for text, probability in zip(texts, probabilities):
    label = "RELEVANT" if probability >= threshold else "NOT_RELEVANT"
    print({"text": text, "probability_positive": float(probability), "label": label})
```

### Sentence-Embedding Baselines

Available embedding baseline names in this run: "embedding-logistic", "embedding-svm", "embedding-lightgbm".

```python
import joblib
from huggingface_hub import hf_hub_download
from sentence_transformers import SentenceTransformer

MODEL_ID = "faodl/agri-trade-classifier"
BASELINE = "embedding-logistic"

texts = [
    "Wheat export inspections rose as demand from importers increased.",
    "The sports ministry announced a new stadium renovation plan.",
]

model_path = hf_hub_download(
    repo_id=MODEL_ID,
    repo_type="model",
    filename=f"baselines/{BASELINE}/{BASELINE}.joblib",
)
artifact = joblib.load(model_path)
embedding_model = SentenceTransformer(artifact["embedding_model_name"])
embeddings = embedding_model.encode(
    texts,
    batch_size=artifact.get("embedding_batch_size", 64),
    convert_to_numpy=True,
    normalize_embeddings=artifact.get("normalize_embeddings", True),
)
probabilities = artifact["classifier"].predict_proba(embeddings)[:, 1]
threshold = artifact["validation_best_threshold"]["threshold"]

for text, probability in zip(texts, probabilities):
    label = "RELEVANT" if probability >= threshold else "NOT_RELEVANT"
    print({"text": text, "probability_positive": float(probability), "label": label})
```

## Files

- `REPORT.md`: Markdown report for this training run.
- `report.json`: Machine-readable report containing metrics and thresholds.
- `transformer/`: Fine-tuned Transformer artifacts, when Transformer training is enabled.
- `baselines/`: TF-IDF and sentence-embedding baseline artifacts, when baseline training is enabled.
- `*/validation_predictions.csv` and `*/test_predictions.csv`: Split-level predictions.