Update README.md

README.md

@@ -5,4 +5,47 @@ language:
 base_model:
 - microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
 pipeline_tag: fill-mask
----
+---
+# Model Card for Model ID
+
+We fine-tuned BiomedBERT using study descriptions from metagenomic projects sourced from MGnify. We applied MLM to unlabelled text data, specifically focusing on the project study descriptions. By fine-tuning the model on domain-specific text, the model now better understands the language and nuances found in metagenomics study description, which helps improve the performance of biome classification tasks.
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** SantiagoSanchezF
+- **Model type:** MLM
+- **Language(s) (NLP):** English
+- **License:** [More Information Needed]
+- **Finetuned from model:** microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
+
+
+### Downstream Use [optional]
+
+This model isthe base of SantiagoSanchezF/trapiche-biome-classifier
+
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+The model was domain adapted by applying masked language modeling (MLM) to a corpus of study descriptions derived from metagenomic projects in MGnify. The input text was tokenized with a maximum sequence length of 256 tokens. A data collator was configured to randomly mask 15% of the input tokens for the MLM task. Training was performed with a batch size of 8, over 3 epochs, and with a learning rate of 5e-5. 
+
+
+## Citation [optional]
+
+TBD
+