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

@@ -8,65 +8,79 @@ tags:
 - trl
 - sft
 - generated_from_trainer
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
 model-index:
 - name: trained_model
   results: []
 ---
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# trained_model
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the generator dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.5432
-- Bertscore Precision: 0.9305
-- Bertscore Recall: 0.9338
-- Bertscore F1: 0.9321
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 2
-- eval_batch_size: 2
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 16
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 5
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Bertscore Precision | Bertscore Recall | Bertscore F1 |
-|:-------------:|:------:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|
-| No log        | 0.9664 | 18   | 1.1003          | 0.8802              | 0.8897           | 0.8849       |
-| 1.7123        | 1.9866 | 37   | 0.6787          | 0.9207              | 0.9228           | 0.9218       |
-| 1.7123        | 2.9530 | 55   | 0.5895          | 0.9300              | 0.9330           | 0.9315       |
-| 0.5828        | 3.9732 | 74   | 0.5516          | 0.9330              | 0.9355           | 0.9342       |
-| 0.4501        | 4.8322 | 90   | 0.5432          | 0.9305              | 0.9338           | 0.9321       |
-
-
-### Framework versions
-
-- PEFT 0.13.0
-- Transformers 4.45.1
-- Pytorch 2.5.1+cpu
-- Datasets 3.0.1
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# trained_model
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the generator dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5432
+- Bertscore Precision: 0.9305
+- Bertscore Recall: 0.9338
+- Bertscore F1: 0.9321
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Bertscore Precision | Bertscore Recall | Bertscore F1 |
+|:-------------:|:------:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|
+| No log        | 0.9664 | 18   | 1.1003          | 0.8802              | 0.8897           | 0.8849       |
+| 1.7123        | 1.9866 | 37   | 0.6787          | 0.9207              | 0.9228           | 0.9218       |
+| 1.7123        | 2.9530 | 55   | 0.5895          | 0.9300              | 0.9330           | 0.9315       |
+| 0.5828        | 3.9732 | 74   | 0.5516          | 0.9330              | 0.9355           | 0.9342       |
+| 0.4501        | 4.8322 | 90   | 0.5432          | 0.9305              | 0.9338           | 0.9321       |
+
+
+### Framework versions
+
+- PEFT 0.13.0
+- Transformers 4.45.1
+- Pytorch 2.5.1+cpu
+- Datasets 3.0.1
 - Tokenizers 0.20.0