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license: mit
tags:
- text-classification
- multi-label-classification
- crypto
- technology
- twitter
- x
- roberta
pipeline_tag: text-classification
---
# Techpto Transformer
RoBERTa multi-label classifier for detecting whether X/Twitter posts or profile text are crypto-related, tech-related, both, or neither.
This is the V5B transformer checkpoint trained for the Techpto classifier project. It was later distilled into the faster `techpto-classifier` hashed linear model for full-corpus scanning.
## Labels
- `crypto`
- `tech`
The model uses sigmoid probabilities, not softmax. A text can match neither label, one label, or both labels.
## Files
- `model.safetensors`: transformer weights.
- `config.json`: `RobertaForSequenceClassification` config with `problem_type = multi_label_classification`.
- `tokenizer.json` and `tokenizer_config.json`: tokenizer files.
- `metrics.json`: full training/eval metrics.
- `classification_thresholds.json`: recommended threshold sets.
## Recommended Thresholds
For high precision on the test split:
```json
{
"crypto": 0.80,
"tech": 0.86
}
```
For best F1 on the test split:
```json
{
"crypto": 0.41,
"tech": 0.57
}
```
For higher recall / F2:
```json
{
"crypto": 0.12,
"tech": 0.16
}
```
## Test Metrics
At the higher-recall thresholds stored in `metrics.json`:
- Exact match accuracy: `0.9071`
- Micro F1: `0.9200`
- Macro F1: `0.9099`
At the high-precision threshold set:
- Exact match accuracy: `0.9305`
- Micro precision: `0.9704`
- Micro recall: `0.8873`
- Micro F1: `0.9270`
- Macro F1: `0.9186`
## Usage
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
repo_id = "pompompur-in/techpto-transformer"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
model.eval()
text = "Building a new AI agent workflow for crypto wallet monitoring."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=192)
with torch.no_grad():
logits = model(**inputs).logits[0]
probs = torch.sigmoid(logits)
labels = ["crypto", "tech"]
thresholds = {"crypto": 0.80, "tech": 0.86}
predictions = {
label: float(prob) >= thresholds[label]
for label, prob in zip(labels, probs)
}
print(dict(zip(labels, map(float, probs))))
print(predictions)
```
## Notes
This checkpoint is intended for classification/research workflows over social text. It is not a general-purpose language model.
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