matthewcym / gense Goto Github PK

from gense import Synthesizer

synthesizer = Synthesizer('mattymchen/nli-synthesizer-t5-base')
input_sents = [
    'The task of judging the best was not easy.',
    'A man plays the piano.'
]

# generate NLI triplets
triplets = synthesizer.generate_triplets(input_sents)

# filter triplets
filtered_triplets = synthesizer.filter_triplets(triplets)

print(filtered_triplets)

from gense import GenSE
gense = GenSE('mattymchen/gense-base-plus')
example_sentences = [
    'An animal is biting a persons finger.',
    'A woman is reading.',
    'A man is lifting weights in a garage.',
    'A man plays the violin.',
    'A man is eating food.',
    'A man plays the piano.',
    'A panda is climbing.',
    'A man plays a guitar.',
    'A woman is slicing a meat.',
    'A woman is taking a picture.'
]
example_queries = [
    'A man is playing music.',
    'A woman is making a photo.'
]

print(gense.encode(example_sentences))

similarities = gense.similarity(example_queries, example_sentences)
print(similarities)

gense.build_index(example_sentences, use_faiss=True)
results = gense.search(example_queries)
for i, result in enumerate(results):
    print("Retrieval results for query: {}".format(example_queries[i]))
    for sentence, score in result:
        print("    {}  (cosine similarity: {:.4f})".format(sentence, score))

from transformers import T5ForConditionalGeneration, AutoTokenizer

# load data synthesis model
synthesis_model = T5ForConditionalGeneration.from_pretrained('mattymchen/nli-synthesizer-t5-base')
synthesis_tokenizer = AutoTokenizer.from_pretrained('mattymchen/nli-synthesizer-t5-base')

# prepare inputs
input_sents = [
    'Write two sentences that are entailment. Sentence 1: \"The task of judging the best was not easy.\"Sentence 2:',
    'Write two sentences that are contradictory. Sentence 1: \"The task of judging the best was not easy.\"Sentence 2:',
    'if \"The task of judging the best was not easy.\", does this mean that \" It was difficult to judge the best.\"? true or false',
    'if \"The task of judging the best was not easy.\", does this mean that \" It was easy to judge the best.\"? true or false'
]
input_features = synthesis_tokenizer(input_sents, add_special_tokens=True, padding=True, return_tensors='pt')

# generation
outputs = synthesis_model.generate(**input_features, top_p=0.9) 

# Outputs:
# It was difficult to judge the best.
# It was easy to judge the best.
# true
# false
print(synthesis_tokenizer.batch_decode(outputs, skip_special_tokens=True))

import torch
from transformers import T5Model, AutoTokenizer

# load embedding model
embedding_model = T5Model.from_pretrained('mattymchen/gense-base-plus').eval()
embedding_tokenizer = AutoTokenizer.from_pretrained('mattymchen/gense-base-plus')

# prepare inputs
input_sents = [
    'The task of judging the best was not easy. Question: what can we draw from the above sentence?',
]
input_features = embedding_tokenizer(input_sents, add_special_tokens=True, padding=True, return_tensors='pt')
decoder_start_token_id = embedding_model._get_decoder_start_token_id()
input_features['decoder_input_ids'] = torch.full([input_features['input_ids'].shape[0], 1], decoder_start_token_id)

# inference
with torch.no_grad():
    outputs = embedding_model(**input_features, output_hidden_states=True, return_dict=True)
    last_hidden = outputs.last_hidden_state
    sent_embs = last_hidden[:, 0].cpu()
print(sent_embs)

from datasets import load_dataset

dataset = load_dataset("mattymchen/synli")