---
language:
- en
license: cc-by-nc-sa-4.0
library_name: transformers
pipeline_tag: text-classification
base_model: xia-lab/FishNALM-20L_pretrain
base_model_relation: finetune
tags:
- DNA
- genomics
- fish
- sequence-classification
- FishNALM
- fine-tuned
- promoter-1k-notata
---

# FishNALM-20L_prom_1k_notata

`FishNALM-20L_prom_1k_notata` is a fine-tuned version of `FishNALM-20L_pretrain` for `Promoter prediction (1 kb, non-TATA promoters)` in fish genomics.

## Model description

This repository contains a **task-specific fine-tuned checkpoint** from the FishNALM model family. The model was initialized from the pretrained base model `FishNALM-20L_pretrain` and then fine-tuned for `Promoter prediction (1 kb, non-TATA promoters)`.

## Task

**Task name**: `Promoter prediction (1 kb, non-TATA promoters)`  
**Task type**: `binary classification`  
**Prediction target**: `promoter vs non-promoter sequences for the non-TATA promoters subset`

Examples:

- CTCF TFBS prediction
- Pou5f1 TFBS prediction
- Sox2 TFBS prediction
- histone modification prediction
- promoter prediction
- splice donor prediction
- splice acceptor prediction
- splice classification

## Base model

- **Base model repository**: `xia-lab/FishNALM-20L_pretrain`
- **Model family**: FishNALM
- **Initialization type**: pretrained checkpoint + downstream fine-tuning

## Training data

This model was fine-tuned on `Promoter prediction (1 kb, non-TATA promoters)` data from `FishGUE`.

## Evaluation

- **Primary metric**: `MCC`
- **Evaluation split / strategy**: `predefined train/validation/test split`

## Intended uses

This model is intended for:

- fish genomics sequence classification
- downstream task inference on sequences similar to the fine-tuning setting
- comparative benchmarking within fish genomic prediction tasks

## Limitations

- This is a **task-specific fine-tuned model** and should be used within the scope of `Promoter prediction (1 kb, non-TATA promoters)`.
- Generalization to other species, tasks, or sequence lengths may be limited.
- This is a research model and is not intended for clinical or diagnostic use.

## How to use

### Load tokenizer and model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo_name = "xia-lab/FishNALM-20L_prom_1k_notata"

tokenizer = AutoTokenizer.from_pretrained(repo_name)
model = AutoModelForSequenceClassification.from_pretrained(repo_name)
```

### Example inference

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

repo_name = "xia-lab/FishNALM-20L_prom_1k_notata"
sequence = "ATGCGTACGTTAGCTAGCTAGCTAGCTAGCTA"

tokenizer = AutoTokenizer.from_pretrained(repo_name)
model = AutoModelForSequenceClassification.from_pretrained(repo_name)

inputs = tokenizer(
    sequence,
    return_tensors="pt",
    truncation=True,
    padding="max_length",
    max_length=512,
)

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

logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
prediction = torch.argmax(probabilities, dim=-1)

print("logits:", logits)
print("probabilities:", probabilities)
print("prediction:", prediction)
```

## Label mapping

- `0`: negative
- `1`: positive

## Files in this repository

Typical files in this repository may include:

- `config.json`
- `model.safetensors`
- `tokenizer.json`
- `tokenizer_config.json`
- `special_tokens_map.json`
- `vocab.txt`
- `README.md`

## Citation

If you use this model, please cite the FishNALM manuscript.

## Contact

For questions, please contact: xqxia@ihb.ac.cn