Update README.md

README.md

@@ -1,10 +1,10 @@
 ---
 license: mit
 datasets:
-- mteb/scifact
+- mteb/banking77
 language:
 - en
-pipeline_tag: text-retrieval
+pipeline_tag: text-classification
 library_name: sentence-transformers
 tags:
 - mteb
@@ -14,57 +14,33 @@ tags:
 - sparse-encoder
 - sparse
 - csr
+
 model-index:
-- name: NV-Embed-v2
+- name: CSR
   results:
-    - dataset:
-        name: MTEB SciFact
-        type: mteb/scifact
-        revision: 0228b52cf27578f30900b9e5271d331663a030d7
-        config: default
-        split: test
-        languages:
-          - eng-Latn
-      metrics:
-        - type: ndcg@1
-          value: 0.67
-        - type: ndcg@3
-          value: 0.7635
-        - type: ndcg@5
-          value: 0.78982
-        - type: ndcg@10
-          value: 0.80426
-        - type: ndcg@20
-          value: 0.80967
-        - type: ndcg@100
-          value: 0.81514
-        - type: ndcg@1000
-          value: 0.81692
-        - type: map@10
-          value: 0.75662
-        - type: map@100
-          value: 0.7593
-        - type: map@1000
-          value: 0.75937
-        - type: recall@10
-          value: 0.93889
-        - type: recall@100
-          value: 0.98667
-        - type: recall@1000
-          value: 1.0
-        - type: precision@1
-          value: 0.67
-        - type: precision@10
-          value: 0.106
-        - type: mrr@10
-          value: 0.76503
-        - type: main_score
-          value: 0.80426
-      task:
-        type: Retrieval
+  - dataset:
+      name: MTEB Banking77Classification
+      type: mteb/banking77
+      config: default
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.899545
+    - type: f1
+      value: 0.899018
+    - type: f1_weighted
+      value: 0.899018
+    - type: main_score
+      value: 0.899545
+    task:
+      type: Classification
+base_model:
+  - nvidia/NV-Embed-v2
 ---
 
 
+
 For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [Github](https://github.com/neilwen987/CSR_Adaptive_Rep).
 
 
@@ -77,18 +53,23 @@ We recommend using ``Transformers 4.47.0.``
 You can evaluate this model loaded by Sentence Transformers with the following code snippet:
 ```python
 import mteb
-from sentence_transformers import SentenceTransformer
-model = SentenceTransformer(
-    "Y-Research-Group/CSR-NV_Embed_v2-Retrieval-SciFACT ",
+from sentence_transformers import SparseEncoder
+model = SparseEncoder(
+    "Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77",
     trust_remote_code=True
 )
 model.prompts = {
-    "SciFact-query": "Instrcut: Given a scientific claim, retrieve documents that support or refute the claim\nQuery:"
+    "Banking77Classification": "Instruct: Given a online banking query, find the corresponding intents\nQuery:"
 }
-task = mteb.get_tasks(tasks=["SciFact"])
+task = mteb.get_tasks(tasks=["Banking77Classification"])
 evaluation = mteb.MTEB(tasks=task)
-evaluation.run(model, eval_splits=["test"], output_folder="./results/SciFact", 
-               batch_size=32, show_progress_bar=True)
+evaluation.run(
+    model,
+    eval_splits=["test"],
+    output_folder="./results/Banking77Classification",
+    show_progress_bar=True
+    encode_kwargs={"convert_to_sparse_tensor": False, "batch_size": 8}
+)  # MTEB don't support sparse tensors yet, so we need to convert to dense tensors
 ```
 
 ## Citation