update README

README.md

@@ -3330,7 +3330,7 @@ model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
 # get the embeddings
 max_length = 4096
 input_texts = queries + passages
-batch_dict = tokenizer(input_texts, max_length=max_length - 1, padding=True, truncation=True, return_tensors="pt")
+batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
 outputs = model(**batch_dict)
 embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
 
@@ -3369,7 +3369,9 @@ print(scores.tolist())
 # [[86.71537780761719, 36.645721435546875], [35.00497055053711, 82.07388305664062]]
 ```
 
-Code for MTEB evaluation will be added soon.
+### MTEB Benchmark Evaluation
+Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB](https://arxiv.org/abs/2210.07316) benchmark.
+
 
 SFR-Embedding Team (∗indicates lead contributors).
 * Rui Meng*