Update README.md

README.md

@@ -21,15 +21,15 @@ This model is intended for various NLP tasks that require understanding and gene
 Bahasa-4b was trained on a 10 billion subset data of Indonesian dataset from a collected pool of 100 billion.
 
 ## Benchmarks
-The following table shows the performance of Bahasa-4b compared to another 4b model, sailor_4b, across several benchmarks:
-
-| Dataset        | Version | Metric | Mode | Sailor_4b | Bahasa-4b-hf |
-|----------------|---------|--------|------|-----------|--------------|
-| tydiqa-id      | 0e9309  | EM     | gen  | 53.98     | 55.04        |
-| tydiqa-id      | 0e9309  | F1     | gen  | 73.48     | 75.39        |
-| xcopa-id       | 36c11c  | EM     | ppl  | 69.2      | 73.2         |
-| xcopa-id       | 36c11c  | F1     | ppl  | 69.2      | 73.2         |
-| m3exam-id-ppl  | ede415  | EM     | ppl  | 31.27     | 44.47        |
-| belebele-id-ppl| 7fe030  | EM     | ppl  | 41.33     | 42.33        |
-
-This data demonstrates that Bahasa-4b consistently outperforms the sailor_4b model in various Indonesian language tasks, showing improvements in both EM (Exact Match) and F1 scores across different datasets.
+The following table shows the performance of Bahasa-4b compared to the models Sailor_4b and Mistral-7B-v0.1 across several benchmarks:
+
+| Dataset        | Version | Metric | Mode | Sailor_4b | Bahasa-4b-hf | Mistral-7B-v0.1 |
+|----------------|---------|--------|------|-----------|--------------|-----------------|
+| tydiqa-id      | 0e9309  | EM     | gen  | 53.98     | 55.04        | 63.54           |
+| tydiqa-id      | 0e9309  | F1     | gen  | 73.48     | 75.39        | 78.73           |
+| xcopa-id       | 36c11c  | EM     | ppl  | 69.2      | 73.2         | 62.40           |
+| xcopa-id       | 36c11c  | F1     | ppl  | 69.2      | 73.2         | -               |
+| m3exam-id-ppl  | ede415  | EM     | ppl  | 31.27     | 44.47        | 26.68           |
+| belebele-id-ppl| 7fe030  | EM     | ppl  | 41.33     | 42.33        | 41.33           |
+
+This data demonstrates that Bahasa-4b consistently outperforms the Sailor_4b model in various Indonesian language tasks, showing improvements in both EM (Exact Match) and F1 scores across different datasets, and is competitive with the Mistral-7B-v0.1 model.