Update README.md

README.md

@@ -1,3 +1,31 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- codefuse-ai/F2LLM
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+---
+
+F2LLM (Foundation to Feature Large Language Models) are foundation models directly finetuned on 6 million high-quality query-document pairs (available in [codefuse-ai/F2LLM](https://huggingface.co/datasets/codefuse-ai/F2LLM)) covering a diverse range of retrieval, classification, and clustering data, curated solely from open-source datasets without any synthetic data. These models are trained with homogeneous macro batches in a single stage, without sophisticated multi-stage pipelines.
+
+To evaluate F2LLMs on MTEB:
+
+```
+import mteb
+import logging
+logging.basicConfig(level=logging.INFO)
+
+task_names = ['AmazonCounterfactualClassification', 'ArXivHierarchicalClusteringP2P', 'ArXivHierarchicalClusteringS2S', 'ArguAna', 'AskUbuntuDupQuestions', 'BIOSSES', 'Banking77Classification', 'BiorxivClusteringP2P.v2', 'CQADupstackGamingRetrieval', 'CQADupstackUnixRetrieval', 'ClimateFEVERHardNegatives', 'FEVERHardNegatives', 'FiQA2018', 'HotpotQAHardNegatives', 'ImdbClassification', 'MTOPDomainClassification', 'MassiveIntentClassification', 'MassiveScenarioClassification', 'MedrxivClusteringP2P.v2', 'MedrxivClusteringS2S.v2', 'SCIDOCS', 'SICK-R', 'STS12', 'STS13', 'STS14', 'STS15', 'STS17', 'STS22.v2', 'STSBenchmark', 'SprintDuplicateQuestions', 'StackExchangeClustering.v2', 'StackExchangeClusteringP2P.v2', 'SummEvalSummarization.v2', 'TRECCOVID', 'Touche2020Retrieval.v3', 'ToxicConversationsClassification', 'TweetSentimentExtractionClassification', 'TwentyNewsgroupsClustering.v2', 'TwitterSemEval2015', 'TwitterURLCorpus', 'MindSmallReranking']
+
+tasks = [
+    mteb.get_task(task_name, languages = ["eng"], eval_splits=["test"], exclusive_language_filter=True)
+    for task_name in task_names
+]
+
+
+model = mteb.get_model("codefuse-ai/F2LLM-1.7B", device="cuda:0")
+evaluation = mteb.MTEB(tasks=tasks)
+evaluation.run(model, encode_kwargs={"batch_size": 16})
+```