Merge branch 'main' of https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct into main

README.md

@@ -5624,9 +5624,9 @@ print(scores.tolist())
 
 ### MTEB & C-MTEB
 
-You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese):
+You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-1.5B-instruct** on MTEB(English)/C-MTEB(Chinese):
 
-| Model Name | MTEB(56)  | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
+| Model Name | MTEB(56)  | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) | 
 |:----:|:---------:|:----------:|:----------:|:----------:|
 | [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   64.23   |     -      |  - | - |
 | [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   63.55   |     -      | - | - |
@@ -5641,8 +5641,9 @@ You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qw
 | [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) |   66.63   |   60.81    |   - | - |
 | [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) |   67.34   |   69.52    |  - | - |
 | [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) |   69.32   |     -      |  - | - |
-| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05**  |  - | - |
-| gte-Qwen2-1.5B-instruc(https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | **70.24** | **72.05**  |  - | - |
+| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05**  | **68.25**   | **67.86** |
+| gte-Qwen2-1.5B-instruc(https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | 67.16 | 67.65  | 66.60 | 64.04 |
+
 ### GTE Models
 
 The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). 
@@ -5659,7 +5660,7 @@ The gte series models have consistently released two types of models: encoder-on
 |        [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)        | English | 8192 |    768    |             0.51GB              |
 | [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |
 |   [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)   | Multilingual | 32000 | 3584 | 26.45GB |
-|   [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)   | Multilingual | 32000 | 1536 | 6.7GB |
+|   [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)   | Multilingual | 32000 | 1536 | 6.62GB |
 ## Citation
 
 If you find our paper or models helpful, please consider cite: