faodl/agri-wheat-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-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: threshold 0.470 (validation F1 0.779); test F1 change vs 0.5: +0.005.
• xgboost_tfidf: threshold 0.520 (validation F1 0.822); test F1 change vs 0.5: -0.001.
• embedding-logistic_sentence_embeddings: threshold 0.521 (validation F1 0.736); test F1 change vs 0.5: +0.004.
• embedding-svm_sentence_embeddings: threshold 0.473 (validation F1 0.745); test F1 change vs 0.5: -0.001.
• embedding-lightgbm_sentence_embeddings: threshold 0.433 (validation F1 0.741); test F1 change vs 0.5: +0.018.
• transformer: threshold 0.203 (validation F1 0.899); test F1 change vs 0.5: +0.010.

Artifacts

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

Inference

Install the runtime dependencies:

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

Transformer

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL_ID = "YOUR_USERNAME/YOUR_MODEL_REPO"

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 = "YOUR_USERNAME/YOUR_MODEL_REPO"
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
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModel, AutoTokenizer

MODEL_ID = "YOUR_USERNAME/YOUR_MODEL_REPO"
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)
tokenizer = AutoTokenizer.from_pretrained(artifact["embedding_model_name"])
encoder = AutoModel.from_pretrained(artifact["embedding_model_name"])
encoder.eval()

encoded_batches = []
batch_size = artifact.get("embedding_batch_size", 64)
for start in range(0, len(texts), batch_size):
    batch_texts = texts[start : start + batch_size]
    inputs = tokenizer(
        batch_texts,
        padding=True,
        truncation=True,
        max_length=artifact.get("embedding_max_length", 256),
        return_tensors="pt",
    )
    with torch.no_grad():
        outputs = encoder(**inputs)
    token_embeddings = outputs.last_hidden_state
    attention_mask = inputs["attention_mask"].unsqueeze(-1).to(token_embeddings.dtype)
    embeddings = (token_embeddings * attention_mask).sum(dim=1)
    embeddings = embeddings / attention_mask.sum(dim=1).clamp(min=1e-9)
    if artifact.get("normalize_embeddings", True):
        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
    encoded_batches.append(embeddings)
embeddings = torch.cat(encoded_batches).numpy()
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.