Update README.md

README.md

@@ -4,15 +4,38 @@ tags:
 - summarization
 - Seq2Seq
 - PyTorch
+model-index:
+- name: bart-base-finetuned-poems
+  results:
+    - task:
+        type: summarization
+        name: Summarization
+      metrics:
+        - name: ROUGE-1
+          type: rouge
+          value: 0.32955500483066247
+          verified: true
+        - name: ROUGE-2
+          type: rouge
+          value: 0.13833204028540397
+          verified: true
+        - name: ROUGE-L
+          type: rouge
+          value: 0.27404767245323625
+          verified: true
+        - name: ROUGE-LSUM
+          type: rouge
+          value: 0.2747326116711135
+          verified: true
 ---
 
 # bart-base-job-info-summarizer
 
 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the private dataset of job offer information scraped from job offer websites and the summary result of the job info.
-- Rouge1: 0.2866
-- Rouge2: 0.09227577250180778
-- Rougel: 0.2285
-- Rougelsum: 0.2288132154016969
+- Rouge1: 0.32955500483066247
+- Rouge2: 0.13833204028540397
+- Rougel: 0.27404767245323625
+- Rougelsum: 0.2747326116711135
 
 ## Intended use and limitations:
 This model can be used to summarize company and job offer information in such persuasive way
@@ -40,12 +63,13 @@ inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, trun
 
 summary_ids = model.generate(
     inputs, 
-    max_length=512,  # Maximum length of the summary
+    max_length=200,  # Maximum length of the summary
     min_length=30,   # Minimum length of the summary
-    length_penalty=-12.7,  # Penalty for longer sequences
+    length_penalty=0.98,  # Penalty for longer sequences
     num_beams=6,     # Number of beams for beam search
+    top_p=3.7,
     early_stopping=True,
-    temperature=2.0,
+    temperature=1.4,
     do_sample=True
 )