Create README.md
Browse filesA nearly useless finetuned version of Mixtral7b
README.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
language: en
|
| 2 |
+
tags:
|
| 3 |
+
- snomed-ct
|
| 4 |
+
- text-generation
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
# My Model Name
|
| 8 |
+
|
| 9 |
+
## Model description
|
| 10 |
+
This is a text generation model for SNOMED-CT. As it is text-generation, it is prone to hallucination and should not be used for any kind of production purpose but it was fun to build. It is based on Mixtral7b and was fine-tuned on a part of the SNOMED-CT corpus then tested against a gold-standard.
|
| 11 |
+
|
| 12 |
+
## How to use
|
| 13 |
+
Provide code snippets on how to use your model.
|
| 14 |
+
|
| 15 |
+
```python
|
| 16 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 17 |
+
|
| 18 |
+
model_name = "{username}/{model_id}"
|
| 19 |
+
model = AutoModelForCausalLM.from_pretrained(model_name)
|
| 20 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| 21 |
+
```
|
| 22 |
+
|
| 23 |
+
Model Performance
|
| 24 |
+
Accuracy: {metrics["0"]}
|
| 25 |
+
Precision: {metrics["0"]}
|
| 26 |
+
Recall: {metrics["0"]}
|
| 27 |
+
|
| 28 |
+
ParameterName SNOMEDCode ExtractedSNOMEDNumbers CorrectPrediction
|
| 29 |
+
*Heart rate 364075005 3222222 False
|
| 30 |
+
|
| 31 |
+
Limitations and bias
|
| 32 |
+
It is really low grade but I had fun building it so have pushed it up
|
| 33 |
+
|
| 34 |
+
Acknowledgments
|
| 35 |
+
Thanks to the Mixtral AI team for creating the base model for this one.
|