update links to new naming scheme

README.md

@@ -19,7 +19,7 @@ SEA-LION stands for _Southeast Asian Languages In One Network_.
 
 ## Model Details
 ### Base model 
-We perform instruction tuning in English and Indonesian on our [pre-trained SEA-LION-7B](https://huggingface.co/aisingapore/sealion7b), a decoder model using the MPT architecture, to create SEA-LION-7B-Instruct.
+We perform instruction tuning in English and Indonesian on our [pre-trained SEA-LION-7B](https://huggingface.co/aisingapore/sea-lion-7b), a decoder model using the MPT architecture, to create SEA-LION-7B-Instruct.
 
 ### Benchmark Performance
 We evaluated SEA-LION-7B-Instruct on the BHASA benchmark ([arXiv](https://arxiv.org/abs/2309.06085v2) and [GitHub](https://github.com/aisingapore/bhasa)) across a variety of tasks. 
@@ -28,15 +28,15 @@ BHASA stands out amongst other evaluations for SEA languages for its holistic ap
 
 The scores shown in the table below have been adjusted to only consider answers provided in the appropriate language.
 
-| Model                          | QA (F1) | Sentiment (F1) | Toxicity (F1) | Eng>Indo (ChrF++) | Indo>Eng (ChrF++) | Summary (ROUGE-L) | NLI (Acc) | Causal (Acc) |
+| Model                          | QA (F1) | Sentiment (F1) | Toxicity (F1) | Eng>Indo (ChrF++) | Indo>Eng (ChrF++) | Summary (ROUGE-L) | NLI (Acc) | Causal (Acc) |
 |--------------------------------|---------|----------------|---------------|-------------------|-------------------|-------------------|-----------|--------------|
-| SEA-LION-7B-Instruct-Research  | 24.86   | 76.13          | 24.45         | 52.50             | 46.82             | 15.44             | 33.20     | 23.80        |
-| SEA-LION-7B-Instruct           | 68.41   | 91.45          | 17.98         | 57.48             | 58.04             | 17.54             | 53.10     | 60.80        |
-| SeaLLM 7B v1                   | 30.96   | 56.29          | 22.60         | 62.23             | 41.55             | 14.03             | 26.50     | 56.60        |
-| SeaLLM 7B v2                   | 44.40   | 80.13          | 55.24         | 64.01             | 63.28             | 17.31             | 43.60     | 82.00        |
-| Sailor-7B                      | 65.43   | 59.48          | 20.48         | 64.27             | 60.68             | 8.69              | 15.10     | 38.40        |
-| Llama 2 7B Chat                | 11.12   | 52.32          | 0.00          | 44.09             | 57.58             | 9.24              | 0.00      | 0.00         |
-| Mistral 7B Instruct v0.1       | 38.85   | 74.38          | 20.83         | 30.60             | 51.43             | 15.63             | 28.60     | 50.80        |
+| SEA-LION-7B-Instruct-Research  | 24.86   | 76.13          | 24.45         | 52.50             | 46.82             | 15.44             | 33.20     | 23.80        |
+| SEA-LION-7B-Instruct           | **68.41**   | **91.45**          | 17.98         | 57.48             | 58.04             | **17.54**             | 53.10     | 60.80        |
+| SeaLLM 7B v1                   | 30.96   | 56.29          | 22.60         | 62.23             | 41.55             | 14.03             | 26.50     | 56.60        |
+| SeaLLM 7B v2                   | 44.40   | 80.13          | **55.24**         | 64.01             | **63.28**             | 17.31             | 43.60     | **82.00**        |
+| Sailor-7B                      | 65.43   | 59.48          | 20.48         | **64.27**             | 60.68             | 8.69              | 15.10     | 38.40        |
+| Llama 2 7B Chat                | 11.12   | 52.32          | 0.00          | 44.09             | 57.58             | 9.24              | 0.00      | 0.00         |
+| Mistral 7B Instruct v0.1       | 38.85   | 74.38          | 20.83         | 30.60             | 51.43             | 15.63             | **53.10**     | 50.80        |
 | GPT-4                          | 73.60   | 74.14          | 63.96         | 69.38             | 67.53             | 18.71             | 83.20     | 96.00        |
 
 ### Usage
@@ -46,8 +46,8 @@ SEA-LION can be run using the 🤗 Transformers library
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("aisingapore/sealion7b-instruct", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("aisingapore/sealion7b-instruct", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("aisingapore/sea-lion-7b-instruct", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("aisingapore/sea-lion-7b-instruct", trust_remote_code=True)
 
 prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n"
 prompt = """Apa sentimen dari kalimat berikut ini?