agri-fertilizers-classifier

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-fertilizers
• Dataset subset: ``
• Text column: chunk_text
• Label column: label
• Transformer: FacebookAI/xlm-roberta-base
• Generated at: 2026-05-19T13:37:49.429687+00:00

Dataset Summary

Threshold Comparison on Test Split

Confusion Matrices on Test Split

Rows are true labels and columns are predicted labels.

logistic_tfidf at threshold 0.500

logistic_tfidf at threshold 0.570

xgboost_tfidf at threshold 0.500

xgboost_tfidf at threshold 0.224

embedding-logistic_sentence_embeddings at threshold 0.500

embedding-logistic_sentence_embeddings at threshold 0.703

embedding-svm_sentence_embeddings at threshold 0.500

embedding-svm_sentence_embeddings at threshold 0.568

embedding-lightgbm_sentence_embeddings at threshold 0.500

embedding-lightgbm_sentence_embeddings at threshold 0.579

transformer at threshold 0.500

transformer at threshold 0.869

Validation-Tuned Thresholds

• logistic_tfidf: threshold 0.570 (validation F1 0.874); test F1 change vs 0.5: -0.017.
• xgboost_tfidf: threshold 0.224 (validation F1 0.898); test F1 change vs 0.5: +0.016.
• embedding-logistic_sentence_embeddings: threshold 0.703 (validation F1 0.841); test F1 change vs 0.5: +0.016.
• embedding-svm_sentence_embeddings: threshold 0.568 (validation F1 0.850); test F1 change vs 0.5: -0.020.
• embedding-lightgbm_sentence_embeddings: threshold 0.579 (validation F1 0.847); test F1 change vs 0.5: -0.003.
• transformer: threshold 0.869 (validation F1 0.911); test F1 change vs 0.5: +0.005.

Artifacts

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

Inference

Install the runtime dependencies:

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

Transformer

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL_ID = "faodl/agri-fertilizers-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".

import json
import joblib
from huggingface_hub import hf_hub_download

MODEL_ID = "faodl/agri-fertilizers-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".

import joblib
from huggingface_hub import hf_hub_download
from sentence_transformers import SentenceTransformer

MODEL_ID = "faodl/agri-fertilizers-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.