Update README.md

README.md

@@ -15,15 +15,15 @@ language:
 - km
 - ta
 ---
-# SEA-LION-7B-Instruct-Research
+# SEA-LION-7B-IT-Research
 
 SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
 The size of the models range from 3 billion to 7 billion parameters.
 This is the card for the SEA-LION 7B Instruct (Non-Commercial) model.
 
-For more details on the base model, please refer to the [base model's model card](https://huggingface.co/aisingapore/sea-lion-7b).
+For more details on the base model, please refer to the [base model's model card](https://huggingface.co/aisingapore/SEA-LION-v1-7B).
 
-For the commercially permissive model, please refer to the [SEA-LION-7B-Instruct](https://huggingface.co/aisingapore/sea-lion-7b-instruct).
+For the commercially permissive model, please refer to the [SEA-LION-7B-IT](https://huggingface.co/aisingapore/SEA-LION-v1-7B-IT).
 
 SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
 
@@ -49,9 +49,9 @@ The model was then further instruction-tuned on <b>Indonesian data only</b>.
 
 ### Benchmark Performance
 
-SEA-LION-7B-Instruct-NC performs better than other models of comparable size when tested on tasks in the Indonesian language.
+SEA-LION-7B-IT-Research performs better than other models of comparable size when tested on tasks in the Indonesian language.
 
-We evaluated SEA-LION-7B-Instruct-NC on the [BHASA benchmark](https://arxiv.org/abs/2309.06085) and 
+We evaluated SEA-LION-7B-IT-Research on the [BHASA benchmark](https://arxiv.org/abs/2309.06085) and 
 compared it against [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
 and [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b-instruct).
 
@@ -69,8 +69,8 @@ For Natural Language Reasoning (NLR) tasks, we tested the model on Natural Langu
 
 | 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        |
+| SEA-LION-7B-IT-Research  | 24.86   | 76.13          | 24.45         | 52.50             | 46.82             | 15.44             | 33.20     | 23.80        |
+| SEA-LION-7B-IT           | **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 (Base)               | 65.43   | 59.48          | 20.48         | **64.27**         | 60.68             | 8.69              | 15.10     | 38.40        |
@@ -83,9 +83,9 @@ For Natural Language Reasoning (NLR) tasks, we tested the model on Natural Langu
 
 ### Model Architecture and Objective
 
-SEA-LION is a decoder model using the MPT architecture.
+SEA-LION-7B-IT-Research is a decoder model using the MPT architecture.
 
-| Parameter       | SEA-LION 7B |
+| Parameter       | SEA-LION-7B-IT-Research |
 |-----------------|:-----------:|
 | Layers          | 32          |
 | d_model         | 4096        |
@@ -107,8 +107,8 @@ The tokenizer type is Byte-Pair Encoding (BPE).
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("aisingapore/sea-lion-7b-instruct-nc", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("aisingapore/sea-lion-7b-instruct-nc", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("aisingapore/SEA-LION-v1-7B-IT-Research", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("aisingapore/SEA-LION-v1-7B-IT-Research", trust_remote_code=True)
 
 prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n"
 prompt = """Apa sentimen dari kalimat berikut ini?