update readme

Author: 545999961

README.md

@@ -38,7 +38,7 @@ FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following p
 
 - **Inference**: [Embedder](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/inference/embedder), [Reranker](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/inference/reranker)
 - **Finetune**: [Embedder](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/finetune/embedder), [Reranker](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/finetune/reranker)
-- **Evaluation**: [MTEB](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/mteb), [BEIR](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/beir), [MSMARCO](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/msmarco), [MIRACL](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/miracl), [MLDR](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/mldr), [MKQA](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/mkqa), [AIR-Bench](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation/air_bench)
+- **Evaluation**: [MTEB](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#1-mteb), [BEIR](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#2-beir), [MSMARCO](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#3-msmarco), [MIRACL](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#4-miracl), [MLDR](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#5-mldr), [MKQA](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#6-mkqa), [AIR-Bench](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#7-air-bench), [Custom Dataset](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#8-custom-dataset)
 - **[Dataset](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/dataset)**: [MLDR](https://huggingface.co/datasets/Shitao/MLDR), [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data), [public-data](https://huggingface.co/datasets/cfli/bge-e5data), [full-data](https://huggingface.co/datasets/cfli/bge-full-data), [reranker-data](Shitao/bge-reranker-data)
 - **[Tutorials](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/Tutorials)**
 - **research**:
@@ -144,7 +144,7 @@ The following contents are releasing in the upcoming weeks:
 </details>
 
 
-## Projects
+## [Projects](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research)
 
 ### BGE-M3 ([Paper](https://arxiv.org/pdf/2402.03216.pdf), [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3))
 
@@ -176,7 +176,7 @@ More details please refer to our [paper](https://arxiv.org/abs/2401.03462) and [
 
 
 ### [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- 
+
 LM-Cocktail automatically merges fine-tuned models and base model using a simple function to compute merging weights.
 LM-Cocktail can be used to improve the performance on target domain without decrease the general capabilities beyond target domain, 
 as well as generate a model for new tasks without fine-tuning.

examples/finetune/reranker/README.md

@@ -75,9 +75,11 @@ See [example_data](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagem
   python add_reranker_score.py \
   --input_file toy_finetune_data_minedHN.jsonl \
   --output_file toy_finetune_data_score.jsonl \
-  --range_for_sampling 2-200 \
-  --negative_number 15 \
-  --use_gpu_for_searching 
+  --reranker_name_or_path BAAI/bge-reranker-v2-m3 \
+  --devices cuda:0 cuda:1 \
+  --cache_dir ./cache/model \
+  --reranker_query_max_length 512 \
+  --reranker_max_length 1024
   ```
 
   - `input_file`: path to save JSON data with mined hard negatives for finetuning