Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,43 +1,55 @@
----
-license: mit
-datasets:
-- sajjadhadi/disease-diagnosis-dataset
-base_model:
-- Qwen/Qwen2.5-3B
-pipeline_tag: text-classification
-tags:
-- biology
-language:
-- en
-library_name: adapter-transformers
----
-
-# Disease Diagnosis Adapter
-
-A fine-tuned adapter for the Qwen/Qwen2.5-3B model specialized in disease diagnosis and classification.
-Trained through MLX and MPI, to test performance and accuracy.
-
-## Overview
-
-This adapter enhances the base Ministral-3b-instruct model to improve performance on medical diagnosis tasks. It was trained on the [disease-diagnosis-dataset](https://huggingface.co/datasets/sajjadhadi/disease-diagnosis-dataset).
-The data is over-saturated in some diagnosis, I limit the number of diagnosis and take a limit number of them as training tags.
-
-
-## Usage
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-# Load model and tokenizer
-model_name = "naifenn/diagnosis-adapter"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Example input
-text = "Patient presents with fever, cough, and fatigue for 3 days."
-inputs = tokenizer(text, return_tensors="pt")
-
-# Get prediction
-outputs = model(**inputs)
-prediction = outputs.logits.argmax(-1).item()
+---
+license: mit
+datasets:
+- sajjadhadi/disease-diagnosis-dataset
+base_model:
+- Qwen/Qwen2.5-3B
+pipeline_tag: text-classification
+tags:
+- biology
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+library_name: adapter-transformers
+---
+
+# Disease Diagnosis Adapter
+
+A fine-tuned adapter for the Qwen/Qwen2.5-3B model specialized in disease diagnosis and classification.
+Trained through MLX and MPI, to test performance and accuracy.
+
+## Overview
+
+This adapter enhances the base Ministral-3b-instruct model to improve performance on medical diagnosis tasks. It was trained on the [disease-diagnosis-dataset](https://huggingface.co/datasets/sajjadhadi/disease-diagnosis-dataset).
+The data is over-saturated in some diagnosis, I limit the number of diagnosis and take a limit number of them as training tags.
+
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "naifenn/diagnosis-adapter"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example input
+text = "Patient presents with fever, cough, and fatigue for 3 days."
+inputs = tokenizer(text, return_tensors="pt")
+
+# Get prediction
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(-1).item()
 print(f"Predicted diagnosis: {model.config.id2label[prediction]}")