Improve CanadianInvertebrates-ML dataset card: metadata, abstract, and sample usage (#2)

- Improve CanadianInvertebrates-ML dataset card: metadata, abstract, and sample usage (71824ed8fc696f1da0e9ff2f25c4efbb7fbbb732)
- Update README: fix text, cite, links, remove placeholder (0216252bbafa0ac8657a15bdeef8738ec87fbcd2)


Co-authored-by: Niels Rogge <[email protected]>
Co-authored-by: Graham Taylor <[email protected]>

README.md

@@ -1,52 +1,56 @@
----
-license: cc-by-3.0
-language:
-- en
-size_categories:
-- 100K<n<1M
-pretty_name: CanInv-ML
-tags:
-- DNA_barcode
-- Taxonomy
-- Biodiversity
-- LLMs
-- BERT
-- Clustering
-- Zero_shot_transfer_learning
-- Insect
-- Species
-maintainers:
-- https://huggingface.co/pmillana
-author:
-  name: Pablo Millan Arias
-  github: https://github.com/millanp95
-  hf: https://huggingface.co/pmillana
-dataset_loader_script: dataset.py
-dataset_split_names:
-- pretrain
-- train
-- validation
-- test
-- test_unseen
-task_categories:
-- feature-extraction
----
+---
+language:
+- en
+license: cc-by-3.0
+size_categories:
+- 1M<n<10M
+task_categories:
+- feature-extraction
+- text-classification
+pretty_name: CanInv-ML
+tags:
+- DNA_barcode
+- Taxonomy
+- Biodiversity
+- LLMs
+- BERT
+- Clustering
+- Zero_shot_transfer_learning
+- Insect
+- Species
+maintainers:
+- https://huggingface.co/pmillana
+author:
+  name: Pablo Millan Arias
+  github: https://github.com/millanp95
+  hf: https://huggingface.co/pmillana
+dataset_loader_script: dataset.py
+dataset_split_names:
+- pretrain
+- train
+- validation
+- test
+- test_unseen
+library_name: transformers
+---
 
 # Dataset Card for CanadianInvertebrates-ML
 
 Alternative names: InvertebratesCanada-ML, CanInv-ML, CanInv-1M, Canada-1.5M
 
+This dataset is used in the paper [BarcodeBERT: Transformers for Biodiversity Analysis](https://huggingface.co/papers/2311.02401).
+
+## Paper Abstract
+In the global challenge of understanding and characterizing biodiversity, short species-specific genomic sequences known as DNA barcodes play a critical role, enabling fine-grained comparisons among organisms within the same kingdom of life. Although machine learning algorithms specifically designed for the analysis of DNA barcodes are becoming more popular, most existing methodologies rely on generic supervised training algorithms. We introduce BarcodeBERT, a family of models tailored to biodiversity analysis and trained exclusively on data from a reference library of 1.5M invertebrate DNA barcodes. We compared the performance of BarcodeBERT on taxonomic identification tasks against a spectrum of machine learning approaches including supervised training of classical neural architectures and fine-tuning of general DNA foundation models. Our self-supervised pretraining strategies on domain-specific data outperform fine-tuned foundation models, especially in identification tasks involving lower taxa such as genera and species. We also compared BarcodeBERT with BLAST, one of the most widely used bioinformatics tools for sequence searching, and found that our method matched BLAST's performance in species-level classification while being 55 times faster. Our analysis of masking and tokenization strategies also provides practical guidance for building customized DNA language models, emphasizing the importance of aligning model training strategies with dataset characteristics and domain knowledge. The code repository is available at this https URL .
 
 ### Overview
-The CanadianInvertebrates-ML is a  machine learning-ready dataset derived from the raw DNA barcodes publiseh in [deWaard  et. al, 2019](https://www.nature.com/articles/s41597-019-0320-2). The data is specifically designed and curated for different machine learning tasks in biodiversity analysis: species classification, genus identification of novel species, and BIN reconstruction.
+CanadianInvertebrates-ML is a machine learning-ready dataset derived from the raw DNA barcodes published in [deWaard et. al, 2019](https://www.nature.com/articles/s41597-019-0320-2). The data is specifically designed and curated for different machine learning tasks in biodiversity analysis: species classification, genus identification of novel species, and BIN reconstruction.
 
 ### Citation
 
-If you make use of this dataset  and/or its code repository, please cite the following paper:
-
-```
-cite as:
+If you make use of this dataset and/or its code repository, please cite the following paper:
 
+```bibtex
 @misc{arias2025barcodeberttransformersbiodiversityanalysis,
       title={BarcodeBERT: Transformers for Biodiversity Analysis}, 
       author={Pablo Millan~Arias and Niousha Sadjadi and Monireh Safari and ZeMing Gong and Austin T. Wang and Joakim Bruslund Haurum and Iuliia Zarubiieva and Dirk Steinke and Lila Kari and Angel X. Chang and Scott C. Lowe and Graham W. Taylor},
@@ -56,11 +60,9 @@ cite as:
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2311.02401}, 
 }
-
-
 ```
 
-## **Dataset content**
+## Dataset content
 
 Each specimen contains the following annotations:
 
@@ -75,15 +77,38 @@ Each specimen contains the following annotations:
   - **Split and localization**  
     - `split`: Data partition label (e.g., pretrain, train, test, val, test_unseen)  
 
-### Sample  Usage
+### Sample Usage
+
+To use this dataset with the BarcodeBERT model, you can follow the example from the associated GitHub repository:
 
-To-Do
+```python
+from transformers import AutoTokenizer, AutoModel
 
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "bioscan-ml/BarcodeBERT", trust_remote_code=True
+)
 
+# Load the model
+model = AutoModel.from_pretrained("bioscan-ml/BarcodeBERT", trust_remote_code=True)
+
+# Sample sequence
+dna_seq = "ACGCGCTGACGCATCAGCATACGA"
+
+# Tokenize
+input_seq = tokenizer(dna_seq, return_tensors="pt")["input_ids"]
+
+# Pass through the model
+output = model(input_seq.unsqueeze(0))["hidden_states"][-1]
+
+# Compute Global Average Pooling
+features = output.mean(1)
+```
+### BIOSCAN Datasets for PyTorch
 
+CanadianInvertebrates-ML is being incorporated into the [BIOSCAN Datasets for PyTorch project](https://github.com/bioscan-ml/dataset), which provides a standardized way to access and utilize this dataset in PyTorch-based machine learning workflows. You can see the work in progress in [this pull request](https://github.com/bioscan-ml/dataset/pull/61).
 ### Dataset Sources
 
 - **GitHub:** https://github.com/bioscan-ml/BarcodeBERT
-- **Zenodo:** To-Do
-- **Kaggle:** ?
+- **Zenodo:** https://doi.org/10.5281/zenodo.15650124
 - **Paper:** https://arxiv.org/abs/2311.02401