Veyra-50M is a compact, encoder-only text classification model developed by Dl26. It is designed for fast English classification across intents, banking support requests, news topics, encyclopedia-style topics, emotion categories, sentiment labels, and broad question topics.

The model uses a BERT-style bidirectional Transformer encoder and the standard Hugging Face Transformers bert model type. It loads directly with AutoModelForSequenceClassification and does not require custom architecture files or trust_remote_code=True.

Veyra-50M is built for practical classification workflows where speed, simple deployment, and a wide label taxonomy matter. It can be used for routing user messages, classifying support requests, enriching metadata, triaging documents, filtering datasets, and building lightweight classifier services.

Why this model

Compact encoder-only classifier
267-label multi-domain taxonomy
Standard Hugging Face Transformers compatibility
No custom Python architecture files
Fast CPU and GPU batch inference
Strong fit for routing, triage, metadata enrichment, and query classification
Supports intent, topic, sentiment, emotion, and support-style labels in one model

Model details

Property	Value
Model name	`Veyra-50M`
Developer	Dl26
Model type	Encoder-only sequence classifier
Transformers model type	`bert`
Architecture	`BertForSequenceClassification`
Parameters	47,814,923
Hidden size	512
Layers	10
Attention heads	8
Intermediate size	2,048
Max positions	512
Number of labels	267
Training objective	Supervised single-label classification
License	Apache 2.0

Label taxonomy

Veyra-50M predicts one label from a namespaced taxonomy. The namespace indicates the task family or source label space.

Family	Labels	Purpose
`clinc_oos`	151	General assistant intent routing and out-of-scope detection
`banking77`	77	Banking and financial support intent classification
`dbpedia_14`	14	Broad entity and encyclopedia topic classification
`yahoo_answers_topics`	10	Broad question topic classification
`emotion`	6	Emotion classification
`sst5`	5	Fine-grained sentiment classification
`ag_news`	4	News topic classification

Example labels:

text

ag_news::Business
ag_news::Sci/Tech
banking77::activate_my_card
banking77::card_payment_not_recognised
clinc_oos::book_flight
clinc_oos::weather
dbpedia_14::Company
dbpedia_14::Film
emotion::joy
emotion::sadness
sst5::4
yahoo_answers_topics::Science & Mathematics

The full label map is stored in config.json as id2label and label2id.

Training data

Veyra-50M was trained on a multi-domain English classification mixture. The training setup combines short user utterances, customer-support style messages, news snippets, topic classification examples, question categories, emotion labels, and sentiment examples.

Source	Train	Validation	Test
`clinc_oos`	15,250	3,100	5,500
`banking77`	9,993	3,076	3,076
`ag_news`	120,000	7,600	7,600
`dbpedia_14`	220,000	8,000	8,000
`yahoo_answers_topics`	220,000	8,000	8,000
`emotion`	16,000	2,000	2,000
`sst5`	8,544	1,101	2,210

The labels are namespaced so that labels from different tasks remain distinct. For example, a banking support label and a general assistant intent label can both describe payments or account activity, but they remain separate targets.

Installation

pip install -U transformers torch accelerate

For CPU-only inference, accelerate is optional:

pip install -U transformers torch

Quick start

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Dl26/Veyra-50M"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

text = "Can you help me reset my password?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)

with torch.no_grad():
    logits = model(**inputs).logits

label_id = int(logits.argmax(dim=-1))
print(model.config.id2label[label_id])

Batch inference

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Dl26/Veyra-50M"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id).to(device)
model.eval()

texts = [
    "Please freeze my card, I think it was stolen.",
    "The company announced a new processor for its laptops.",
    "I feel really happy about the result today.",
    "Who won the match last night?",
]

inputs = tokenizer(
    texts,
    return_tensors="pt",
    truncation=True,
    padding=True,
    max_length=64,
).to(device)

with torch.no_grad():
    logits = model(**inputs).logits

for text, label_id in zip(texts, logits.argmax(dim=-1).tolist()):
    print(model.config.id2label[label_id], "-", text)

Confidence scores

python

import torch

with torch.no_grad():
    logits = model(**inputs).logits
    scores = torch.softmax(logits, dim=-1)

top_scores, top_ids = scores.topk(k=3, dim=-1)

for row_scores, row_ids in zip(top_scores, top_ids):
    for score, label_id in zip(row_scores.tolist(), row_ids.tolist()):
        print(model.config.id2label[label_id], round(score, 4))

Confidence scores are useful for routing and fallback behavior. For production systems, calibrate thresholds on data from the target application.

Evaluation highlights

Evaluation	Result
Validation accuracy	83.15%
Validation macro-F1	85.28%
Test accuracy	80.65%
Test macro-F1	83.31%
Remote code required	No

Intended use

Veyra-50M is intended for:

intent classification
support request routing
customer-service triage
topic classification
emotion and sentiment classification
metadata enrichment
dataset filtering
search and recommendation labeling
lightweight classification APIs
encoder model research

Input formatting

The model works best with short to medium English text. For records with multiple fields, combine the fields into a single natural-language string before classification.

text = f"{title}. {description}"

Recommended defaults:

tokenizer(text, truncation=True, padding=True, max_length=64)

Use max_length=128 or higher when classifying longer documents, but expect slower inference.

Limitations

Veyra-50M is a classifier, not a generative model.
The model predicts within its fixed 267-label taxonomy.
It is trained for English text.
Very short, vague, adversarial, or out-of-domain inputs may be ambiguous.
Confidence scores may need calibration for production use.
Human review is recommended for high-impact decisions.

Citation

bibtex

@misc{dl26_2026_veyra_50m,
  title        = {Veyra-50M: Fast Multi-Domain Text Classification},
  author       = {Dl26},
  year         = {2026},
  url          = {https://huggingface.co/Dl26/Veyra-50M}
}