Kelmeilia commited on
Commit
275704c
·
verified ·
1 Parent(s): 7e04e3a

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +51 -4
README.md CHANGED
@@ -1,14 +1,32 @@
1
  ---
2
  library_name: transformers
3
- tags: []
 
 
 
 
4
  ---
5
 
6
  # Model Card for Model ID
7
 
8
- <!-- Provide a quick summary of what the model is/does. -->
9
 
 
10
 
 
 
 
 
 
 
 
 
 
 
 
 
11
 
 
12
  ## Model Details
13
 
14
  ### Model Description
@@ -35,7 +53,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
35
 
36
  ## Uses
37
 
38
- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
 
40
  ### Direct Use
41
 
@@ -71,10 +89,39 @@ Users (both direct and downstream) should be made aware of the risks, biases and
71
 
72
  Use the code below to get started with the model.
73
 
74
- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
75
 
 
 
 
 
 
 
 
76
  ## Training Details
77
 
 
 
78
  ### Training Data
79
 
80
  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
1
  ---
2
  library_name: transformers
3
+ datasets:
4
+ - neil-code/dialogsum-test
5
+ base_model:
6
+ - TinyLlama/TinyLlama-1.1B-Chat-v1.0
7
+ pipeline_tag: summarization
8
  ---
9
 
10
  # Model Card for Model ID
11
 
12
+ This model summarizes dialogues between two persons.
13
 
14
+ This is a sample input for the model:
15
 
16
+ <PRE>
17
+
18
+ Instruct: Summarize the following conversation.
19
+ #Person1#: Happy Birthday, this is for you, Brian.
20
+ #Person2#: I'm so happy you remember, please come in and enjoy the party. Everyone's here, I'm sure you have a good time.
21
+ #Person1#: Brian, may I have a pleasure to have a dance with you?
22
+ #Person2#: Ok.
23
+ #Person1#: This is really wonderful party.
24
+ #Person2#: Yes, you are always popular with everyone. and you look very pretty today.
25
+ #Person1#: Thanks, that's very kind of you to say. I hope my necklace goes with my dress, and they both make me look good I feel.
26
+ #Person2#: You look great, you are absolutely glowing.
27
+ #Person1#: Thanks, this is a fine party. We should have a drink together to celebrate your birthday
28
 
29
+ </PRE>
30
  ## Model Details
31
 
32
  ### Model Description
 
53
 
54
  ## Uses
55
 
56
+ Format dialogue in accord to the sample prompt and you get a summary of the dialogue
57
 
58
  ### Direct Use
59
 
 
89
 
90
  Use the code below to get started with the model.
91
 
92
+ ```
93
+ import torch
94
+ from transformers import AutoTokenizer, AutoModelForCausalLM
95
+
96
+ model_name = "Kelmeilia/llama1_1chat-dialogsum-finetuned"
97
+ model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto', torch_dtype=torch.float16, is_trainable=False)
98
+
99
+ eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True, use_fast=False)
100
+ eval_tokenizer.pad_token = eval_tokenizer.eos_token
101
+
102
+ dialogue = """ Joona: Can I have a banana, Ivana?
103
+ Ivana: No, I am out of bananas.
104
+ Joona: Give me an apple then?
105
+ Ivana: Sure, here you go
106
+ """
107
+
108
+ prompt = f"Instruct: Summarize the following conversation.\n{dialogue}\nOutput:\n"
109
+
110
+ tokens = eval_tokenizer(p, return_tensors="pt")
111
+ result = model.generate(**tokens.to("cuda"), max_new_tokens=100, do_sample=True,num_return_sequences=1,temperature=0.1,num_beams=1,top_p=0.95,).to('cpu')
112
+ output = eval_tokenizer.batch_decode(result, skip_special_tokens=True)
113
 
114
+
115
+
116
+ dialogue_summary_str = output[0].split('Output:\n')[1]
117
+
118
+ print(dialogue_summary_str)
119
+
120
+ ```
121
  ## Training Details
122
 
123
+ 500 steps of Lora Finetuning
124
+
125
  ### Training Data
126
 
127
  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->