def main(args):
# Read the dataset
df = pd.read_csv(args.file)
embedder = BertWrapper(args.model_path, max_seq_length=256)
pooler = PoolingLayer(embedder.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
pooling_mode_cls_token=False,
pooling_mode_max_tokens=False,
layer_to_use=args.layer)
model = SentenceEncoder(modules=[embedder, pooler])
model.eval()
evaluator = EmbeddingSimilarityEvaluator(
main_similarity=SimilarityFunction.COSINE)
if args.t2s:
df["text_1"] = df["text_1"].apply(convert_t2s)
df["text_2"] = df["text_2"].apply(convert_t2s)
tmp = model.encode(df["text_1"].tolist() + df["text_2"].tolist(),
batch_size=16,
show_progress_bar=True)
embeddings1, embeddings2 = tmp[:df.shape[0]], tmp[df.shape[0]:]
spearman_score = evaluator(embeddings1,
embeddings2,
labels=df["similarity"].values)
print(spearman_score)
preds = 1 - paired_cosine_distances(embeddings1, embeddings2)
df["pred"] = preds
df.to_csv("cache/annotated_zero_shot_pred.csv", index=False)
print(f"Pred {pd.Series(preds).describe()}")
return preds, df["similarity"].values
def main(args):
encoder = SentenceEncoder(model_path=args.model_path)
encoder.eval()
if APEX and args.amp and (not args.torchscript):
encoder = amp.initialize(encoder, opt_level=args.amp)
if args.torchscript:
if args.amp:
encoder[0].bert = encoder[0].bert.half()
traced_model = torch.jit.trace(
encoder[0].bert,
(torch.zeros(8, 256).long().cuda(),
torch.zeros(8, 256).long().cuda(),
torch.ones(8, 256).long().cuda())
)
encoder[0].bert = traced_model
assert isinstance(encoder[0].bert, torch.jit.TopLevelTracedModule)
encoder.max_seq_length = 256
print(encoder[1].get_config_dict())
encoder[1].pooling_mode_cls_token = False
encoder[1].pooling_mode_mean_tokens = True
print(encoder[1].get_config_dict())
preds, _ = raw(args, encoder)
print(f"Pred {pd.Series(preds).describe()}")