---
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-production-v03`
- Dataset subset: ``
- Text column: `chunk_text`
- Label column: `label`
- Transformer: `FacebookAI/xlm-roberta-base`
- Generated at: `2026-05-19T18:39:45.207640+00:00`

## Dataset Summary

| Split | Rows | Label 0 | Label 1 | Unique groups | Mean text length |
| --- | ---: | ---: | ---: | ---: | ---: |
| train | 5937 | 4830 | 1107 |  | 670.8 |
| validation | 1280 | 1011 | 269 |  | 678.0 |
| test | 1388 | 1106 | 282 |  | 659.2 |

## Threshold Comparison on Test Split

| Model | Threshold | Accuracy | Precision | Recall | F1 | ROC AUC | Average precision |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| logistic_tfidf | 0.500 | 0.899 | 0.741 | 0.773 | 0.757 | 0.934 | 0.813 |
| logistic_tfidf | 0.479 | 0.897 | 0.729 | 0.784 | 0.756 | 0.934 | 0.813 |
| xgboost_tfidf | 0.500 | 0.898 | 0.872 | 0.582 | 0.698 | 0.916 | 0.791 |
| xgboost_tfidf | 0.197 | 0.878 | 0.674 | 0.777 | 0.722 | 0.916 | 0.791 |
| embedding-logistic_sentence_embeddings | 0.500 | 0.880 | 0.646 | 0.904 | 0.753 | 0.949 | 0.812 |
| embedding-logistic_sentence_embeddings | 0.505 | 0.880 | 0.647 | 0.904 | 0.754 | 0.949 | 0.812 |
| embedding-svm_sentence_embeddings | 0.500 | 0.885 | 0.728 | 0.691 | 0.709 | 0.947 | 0.807 |
| embedding-svm_sentence_embeddings | 0.261 | 0.883 | 0.663 | 0.865 | 0.751 | 0.947 | 0.807 |
| embedding-lightgbm_sentence_embeddings | 0.500 | 0.890 | 0.741 | 0.709 | 0.725 | 0.950 | 0.810 |
| embedding-lightgbm_sentence_embeddings | 0.161 | 0.896 | 0.704 | 0.837 | 0.765 | 0.950 | 0.810 |
| transformer | 0.500 | 0.901 | 0.733 | 0.809 | 0.769 | 0.957 | 0.827 |
| transformer | 0.376 | 0.896 | 0.696 | 0.869 | 0.773 | 0.957 | 0.827 |

## 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 | 1030 | 76 |
| RELEVANT | 64 | 218 |

### logistic_tfidf at threshold 0.479

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1024 | 82 |
| RELEVANT | 61 | 221 |

### xgboost_tfidf at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1082 | 24 |
| RELEVANT | 118 | 164 |

### xgboost_tfidf at threshold 0.197

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1000 | 106 |
| RELEVANT | 63 | 219 |

### embedding-logistic_sentence_embeddings at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 966 | 140 |
| RELEVANT | 27 | 255 |

### embedding-logistic_sentence_embeddings at threshold 0.505

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 967 | 139 |
| RELEVANT | 27 | 255 |

### embedding-svm_sentence_embeddings at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1033 | 73 |
| RELEVANT | 87 | 195 |

### embedding-svm_sentence_embeddings at threshold 0.261

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 982 | 124 |
| RELEVANT | 38 | 244 |

### embedding-lightgbm_sentence_embeddings at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1036 | 70 |
| RELEVANT | 82 | 200 |

### embedding-lightgbm_sentence_embeddings at threshold 0.161

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1007 | 99 |
| RELEVANT | 46 | 236 |

### transformer at threshold 0.500

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 1023 | 83 |
| RELEVANT | 54 | 228 |

### transformer at threshold 0.376

| True / Predicted | NOT_RELEVANT | RELEVANT |
| --- | ---: | ---: |
| NOT_RELEVANT | 999 | 107 |
| RELEVANT | 37 | 245 |


## Validation-Tuned Thresholds

- `logistic_tfidf`: threshold `0.479` (validation F1 `0.778`); test F1 change vs 0.5: `-0.001`.
- `xgboost_tfidf`: threshold `0.197` (validation F1 `0.717`); test F1 change vs 0.5: `+0.024`.
- `embedding-logistic_sentence_embeddings`: threshold `0.505` (validation F1 `0.797`); test F1 change vs 0.5: `+0.001`.
- `embedding-svm_sentence_embeddings`: threshold `0.261` (validation F1 `0.785`); test F1 change vs 0.5: `+0.042`.
- `embedding-lightgbm_sentence_embeddings`: threshold `0.161` (validation F1 `0.815`); test F1 change vs 0.5: `+0.040`.
- `transformer`: threshold `0.376` (validation F1 `0.822`); test F1 change vs 0.5: `+0.004`.

## Artifacts

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