Update README.md

README.md

@@ -1,36 +1,43 @@
 ---
 base_model: google/gemma-3-270m-it
 library_name: transformers
-model_name: checkpoints
+model_name: cypher-gemma
 tags:
 - generated_from_trainer
 - trl
 - sft
+- cypher
 licence: license
+datasets:
+- neo4j/text2cypher-2025v1
+pipeline_tag: text-generation
+language:
+- en
 ---
 
-# Model Card for checkpoints
+# Model Card
+
+This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). Its purpose is turning natural language queries into CypherQueryLanguage.
 
-This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
 
 ```python
 from transformers import pipeline
+from schemas import MOVIE_SCHEMA # you need to define this yourself!
+
+query = "Which actors played a role in the movie Titanic?"
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="VoErik/checkpoints", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+pipe = pipeline("text-generation", model="VoErik/cypher-gemma", device="cuda")
+output = pipe([{"role": "user", "content": f"Question: {question} \n Schema: {MOVIE_SCHEMA}"}], max_new_tokens=256, return_full_text=False)[0]
 print(output["generated_text"])
 ```
 
 ## Training procedure
 
- 
-
-
-This model was trained with SFT.
+This model was trained with SFT on the text2cypher-2025v1 dataset from Neo4j.
+It was trained for roughly 3500 steps.
 
 ### Framework versions
 
@@ -42,8 +49,6 @@ This model was trained with SFT.
 
 ## Citations
 
-
-
 Cite TRL as:
     
 ```bibtex