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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-300-tata
---
# FishNALM-20L_prom_300_tata
`FishNALM-20L_prom_300_tata` is a fine-tuned version of `FishNALM-20L_pretrain` for `Promoter prediction (300 bp, 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 (300 bp, TATA promoters)`.
## Task
**Task name**: `Promoter prediction (300 bp, TATA promoters)`
**Task type**: `binary classification`
**Prediction target**: `promoter vs non-promoter sequences for the 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 (300 bp, 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 (300 bp, 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_300_tata"
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_300_tata"
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
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