update readme

README.md

@@ -1,3 +1,49 @@
 ---
 license: apache-2.0
+datasets:
+- winddude/finacial_pharsebank_66agree_split
+- financial_phrasebank
+language:
+- en
+metrics:
+- accuracy
+model-index:
+- name: financial-sentiment-analysis
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: financial_phrasebank
+      type: financial_phrasebank
+      args: sentences_66agree
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.84
+pipeline_tag: text-classification
+tags:
+- finance
+- sentiment
 ---
+
+# Mamba Finacial Headline Sentiment
+
+Score 0.84 on accuracy for the finacial phrasebank dataset. A completely huggingface capitable implementation of sequence classification with mamba using: <https://github.com/getorca/mamba_for_sequence_classification>.
+
+## Inference:
+
+```
+from transformers import pipeline
+
+
+model_path = 'winddude/mamba_finacial_phrasebank_sentiment'
+
+classifier = pipeline("text-classification", model=model_path, trust_remote_code=True)
+
+text = "Finnish retail software developer Aldata Solution Oyj reported a net loss of 11.7 mln euro $ 17.2 mln for 2007 versus a net profit of 2.5 mln euro $ 3.7 mln for 2006 ."
+
+classifier(text)
+```
+gives:
+`[{'label': 'NEGATIVE', 'score': 0.8793253302574158}]`