README.md

---
language: en
license: mit
library_name: transformers
tags:
  - text-classification
  - onnx
  - freelance
  - intent-classification
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: text-classification
widget:
  - text: "I offer web scraping and automation services"
    example_title: "Offer example"
  - text: "Need a copywriter for email sequences"
    example_title: "Request example"
  - text: "Hi everyone, hope you're doing well today"
    example_title: "Neither example"
---

# Freelance offer/request classifier

Fine-tuned `all-MiniLM-L6-v2` (22M params) that classifies short freelance-community
messages into one of three categories:

- **offer** — the sender is offering their own service/skill
- **request** — the sender is looking to hire / needs someone else's service
- **neither** — general chat, unrelated to offering/requesting a service

## Performance

Evaluated on a held-out validation split (15% of training data, not seen during training):

| Class    | Precision | Recall | F1   |
|----------|-----------|--------|------|
| offer    | 0.74      | 0.87   | 0.80 |
| request  | 0.93      | 0.88   | 0.90 |
| neither  | 0.86      | 0.78   | 0.82 |
| **accuracy** |       |        | **0.85** |
| **macro avg** | 0.84 | 0.84  | **0.84** |

Trained on 2,614 messages collected from freelance-community Telegram groups.

## Known limitation

The model still struggles with the phrasing pattern "I'm looking for [role]
opportunities" when the speaker is actually **offering** their own skill
(it tends to predict "request" instead of "offer" for this pattern with high
confidence). If your use case is sensitive to this, consider a post-processing
rule for this specific phrasing, or contribute additional labeled examples.

## Usage (ONNX, recommended — fast, CPU-only)

```python
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("ademchaoua/freelance-offer-request-classifier", subfolder="onnx")
model = ORTModelForSequenceClassification.from_pretrained("ademchaoua/freelance-offer-request-classifier", subfolder="onnx")

inputs = tokenizer("I offer web scraping services", return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logits.argmax(-1).item()
print(model.config.id2label[pred])
```

## Usage (PyTorch)

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

tokenizer = AutoTokenizer.from_pretrained("ademchaoua/freelance-offer-request-classifier")
model = AutoModelForSequenceClassification.from_pretrained("ademchaoua/freelance-offer-request-classifier")

inputs = tokenizer("Need a copywriter for email sequences", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
pred = logits.argmax(-1).item()
print(model.config.id2label[pred])
```

## Training details

- Base model: `sentence-transformers/all-MiniLM-L6-v2`
- Method: end-to-end fine-tuning (full model, classification head included)
- Max sequence length: 96 tokens
- Class-weighted loss (balanced) to counter class imbalance
- Early stopping on macro-F1
- Trained on ~2,600 messages collected from freelance-community Telegram groups,
  labeled with an LLM (DeepSeek) and manually reviewed
- Exported to ONNX + INT8 quantized for fast CPU inference (~2ms/text)