File size: 4,263 Bytes
fe11772 8bcfbaa fe11772 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
import torch
import json
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from datasets import load_dataset
from tqdm import tqdm
device_map = "auto"
model = AutoModelForCausalLM.from_pretrained(
"/path/to/llamipa/adapter",
return_dict=True,
torch_dtype=torch.float16,
device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained("/tmpdir/thompson/Meta-Llama-3-8B/",add_eos_token=True)
tokenizer.pad_token_id = tokenizer.eos_token_id + 1
tokenizer.padding_side = "right"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_new_tokens=100)
print("Device:",model.hf_device_map)
test_dataset = load_dataset("json", data_files={'test':'/path/to/parser_test_15_incremental.jsonl'})["test"]
def check_endpoints(struct, head):
"""
takes a struct string and a head int and returns only
the struct rels with sources that are >= head
"""
new_rels_list = []
new_rels = None
if struct:
rels = struct.split(' ')
for rel in rels:
if len(rel) > 0:
source = int(rel.split('(')[1].split(',')[0].strip())
if source >= head:
new_rels_list.append(rel)
if len(new_rels_list) > 0:
new_rels = ' '.join(new_rels_list)
return new_rels
def add_previous(sample, previous, predictions):
new_output = []
keep_str = None
#get head
slist = sample.split('\n')
head = int(slist[0].split('Context:')[1].split('<')[0].strip())
# check current structure
for s in slist:
if s.startswith('Structure:'):
new_structure = check_endpoints(previous, head)
if new_structure:
s = 'Structure: ' + new_structure + ' ' + predictions
keep_str = new_structure + ' ' + predictions
else:
s = 'Structure: ' + predictions
keep_str = predictions
new_output.append(s)
new_output_string = '\n'.join(new_output)
return keep_str, new_output_string
def format_gen(preds):
labels = ['COM','CONTR','CORR','QAP','ACK','ELAB','CLARIFQ','COND','CONTIN',
'RES','EXPL','QELAB','ALT','NARR','CONFQ','SEQ']
split_list = [st.strip() for st in preds.split(' ')]
clean_list = []
for a in split_list:
s_tuple = None
rel = None
try:
s = a.split('(')[1].split(')')[0].split(',')
r = a.split('(')[0].strip()
except IndexError:
print('split error one')
else:
try:
s_tuple = (int(s[0]), int(s[1]))
except IndexError:
print('split error two')
except ValueError:
print('value error three')
if r in labels:
#make sure the label is well-formed
rel = r
if rel != None and s_tuple != None:
clean_list.append(rel + '(' + str(s_tuple[0]) + ',' + str(s_tuple[1]) + ')')
clean_preds = ' '.join(clean_list)
return clean_preds
def formatting_prompts_func(example):
output_text = '<|begin_of_text|>Identify the discourse structure (DS) for the new turn in the following excerpt :\n ' + example + '\n ### DS:'
return output_text
f = open("/path/to/test-output-file.txt","w")
new_generations = None
previous_generations = None
new_dialogue = 0
for datum in tqdm(test_dataset['sample']):
if datum.startswith('NEW DIALOGUE'):
print("cond satisfied")
new_dialogue = 1
continue
#figure out if it's a first example
if new_dialogue:
text = formatting_prompts_func(datum)
previous_generations = None
new_dialogue = 0
else:
#need to make sure head edu and relations match up
update_prev, amended_text = add_previous(datum, previous_generations, new_generations)
previous_generations = update_prev
text = formatting_prompts_func(amended_text)
#print(text)
generated = pipe(text)[0]['generated_text']
# generated = 'CORR(8,9) CLARIFQ(9,10) QAP(10,11) COM(10,12) ELAB(12,13) CORR(8,14) RES(11,14) CONFQ(14,15)'
print(generated, file=f)
new_generations = format_gen(generated.split('### DS:')[1])
f.close()
|