Aygun commited on
Commit
5106c20
·
verified ·
1 Parent(s): 77ea62b

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +20 -19
README.md CHANGED
@@ -31,24 +31,6 @@ This is the model card for **phi-2-dialogsum**, a dialogue summarization model b
31
  ### Direct Use
32
  This model can be used directly for **dialogue summarization** tasks. For example, given a multi-turn conversation, the model will produce a succinct summary capturing the key information and context.
33
 
34
- ### Downstream Use [optional]
35
- Could be fine-tuned or adapted for other text summarization tasks, especially conversation-like data (customer service transcripts, chat logs, interviews, etc.).
36
-
37
- ### Out-of-Scope Use
38
- - Generating harmful or misleading content.
39
- - Deploying in high-stakes scenarios without proper validation (e.g., medical or legal advice).
40
-
41
- ## Bias, Risks, and Limitations
42
-
43
- - **Biases:** The model may reflect biases present in the data used to train or fine-tune it.
44
- - **Risks:** Summaries could omit critical context or misrepresent the conversation.
45
- - **Limitations:** The model’s performance may degrade on conversations with specialized jargon, code-switching, or extremely long contexts.
46
-
47
- ### Recommendations
48
- - Always review generated summaries for accuracy.
49
- - Be mindful of potential biases or omissions.
50
- - Avoid using the model as the sole source of truth in sensitive domains.
51
-
52
  ## How to Get Started with the Model
53
 
54
  Below is a quick code snippet to load and run inference with this model:
@@ -69,6 +51,25 @@ inputs = tokenizer([input_text], max_length=512, truncation=True, return_tensors
69
  summary_ids = model.generate(**inputs, max_length=60, num_beams=4, early_stopping=True)
70
  summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
71
 
72
- print("Summary:", summary)```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
73
 
 
 
 
 
74
 
 
31
  ### Direct Use
32
  This model can be used directly for **dialogue summarization** tasks. For example, given a multi-turn conversation, the model will produce a succinct summary capturing the key information and context.
33
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
34
  ## How to Get Started with the Model
35
 
36
  Below is a quick code snippet to load and run inference with this model:
 
51
  summary_ids = model.generate(**inputs, max_length=60, num_beams=4, early_stopping=True)
52
  summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
53
 
54
+ print("Summary:", summary)
55
+ ```
56
+
57
+ ## Training Details
58
+
59
+ Training dataset (Dialogsum)[https://huggingface.co/datasets/neil-code/dialogsum-test]
60
+
61
+ ## Evaluation
62
+
63
+ ORIGINAL MODEL:
64
+ {'rouge1': 0.2990526195120211, 'rouge2': 0.10874019046839419, 'rougeL': 0.21186900909813286, 'rougeLsum': 0.22342464591439556}
65
+
66
+ PEFT MODEL:
67
+ {'rouge1': 0.3132817683433486, 'rouge2': 0.1070363134080079, 'rougeL': 0.23226760188839027, 'rougeLsum': 0.25947902747914586}
68
+
69
+ ## Absolute percentage improvement of PEFT MODEL over ORIGINAL MODEL
70
 
71
+ rouge1: 1.42%
72
+ rouge2: -0.17%
73
+ rougeL: 2.04%
74
+ rougeLsum: 3.61%
75