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-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

Inference

Install the runtime dependencies:

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

Transformer

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL_ID = "faodl/agri-utilization-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-utilization-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
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModel, AutoTokenizer

MODEL_ID = "faodl/agri-utilization-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)
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.