forked from filipg/aitech-eks-pub
2.2 MiB
2.2 MiB
Wizualizacja atencji
!pip install bertviz
Requirement already satisfied: bertviz in /home/kuba/anaconda3/lib/python3.8/site-packages (1.1.0) Requirement already satisfied: torch>=1.0 in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (1.7.1) Requirement already satisfied: boto3 in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (1.17.93) Requirement already satisfied: transformers>=2.0 in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (4.2.2) Requirement already satisfied: sentencepiece in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (0.1.95) Requirement already satisfied: requests in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (2.24.0) Requirement already satisfied: regex in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (2020.6.8) Requirement already satisfied: tqdm in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (4.47.0) Requirement already satisfied: numpy in /home/kuba/anaconda3/lib/python3.8/site-packages (from torch>=1.0->bertviz) (1.18.5) Requirement already satisfied: typing-extensions in /home/kuba/anaconda3/lib/python3.8/site-packages (from torch>=1.0->bertviz) (3.7.4.2) Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/kuba/anaconda3/lib/python3.8/site-packages (from boto3->bertviz) (0.10.0) Requirement already satisfied: s3transfer<0.5.0,>=0.4.0 in /home/kuba/anaconda3/lib/python3.8/site-packages (from boto3->bertviz) (0.4.2) Requirement already satisfied: botocore<1.21.0,>=1.20.93 in /home/kuba/anaconda3/lib/python3.8/site-packages (from boto3->bertviz) (1.20.93) Requirement already satisfied: sacremoses in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (0.0.43) Requirement already satisfied: packaging in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (20.4) Requirement already satisfied: tokenizers==0.9.4 in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (0.9.4) Requirement already satisfied: filelock in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (3.0.12) Requirement already satisfied: certifi>=2017.4.17 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (2020.6.20) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (1.25.9) Requirement already satisfied: chardet<4,>=3.0.2 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (3.0.4) Requirement already satisfied: idna<3,>=2.5 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (2.10) Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/kuba/anaconda3/lib/python3.8/site-packages (from botocore<1.21.0,>=1.20.93->boto3->bertviz) (2.8.1) Requirement already satisfied: click in /home/kuba/anaconda3/lib/python3.8/site-packages (from sacremoses->transformers>=2.0->bertviz) (7.1.2) Requirement already satisfied: six in /home/kuba/anaconda3/lib/python3.8/site-packages (from sacremoses->transformers>=2.0->bertviz) (1.15.0) Requirement already satisfied: joblib in /home/kuba/anaconda3/lib/python3.8/site-packages (from sacremoses->transformers>=2.0->bertviz) (0.16.0) Requirement already satisfied: pyparsing>=2.0.2 in /home/kuba/anaconda3/lib/python3.8/site-packages (from packaging->transformers>=2.0->bertviz) (2.4.7)
from transformers import AutoTokenizer, AutoModel
from bertviz import model_view, head_view
TEXT = "This is a sample input sentence for a transformer model"
MODEL = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL, output_attentions=True)
inputs = tokenizer.encode(TEXT, return_tensors='pt')
outputs = model(inputs)
attention = outputs[-1]
tokens = tokenizer.convert_ids_to_tokens(inputs[0])
SELF ATTENTION MODELS
head_view(attention, tokens)
Layer:
model_view(attention, tokens)
ENCODER-DECODER MODELS
MODEL = "Helsinki-NLP/opus-mt-en-de"
TEXT_ENCODER = "She sees the small elephant."
TEXT_DECODER = "Sie sieht den kleinen Elefanten."
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL, output_attentions=True)
encoder_input_ids = tokenizer(TEXT_ENCODER, return_tensors="pt", add_special_tokens=True).input_ids
decoder_input_ids = tokenizer(TEXT_DECODER, return_tensors="pt", add_special_tokens=True).input_ids
outputs = model(input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids)
encoder_text = tokenizer.convert_ids_to_tokens(encoder_input_ids[0])
decoder_text = tokenizer.convert_ids_to_tokens(decoder_input_ids[0])
head_view(
encoder_attention=outputs.encoder_attentions,
decoder_attention=outputs.decoder_attentions,
cross_attention=outputs.cross_attentions,
encoder_tokens= encoder_text,
decoder_tokens = decoder_text
)
Layer:
Attention: Encoder
Decoder
Cross
model_view(
encoder_attention=outputs.encoder_attentions,
decoder_attention=outputs.decoder_attentions,
cross_attention=outputs.cross_attentions,
encoder_tokens= encoder_text,
decoder_tokens = decoder_text
)
Attention: Encoder
Decoder
Cross
Zadanie (10 minut)
Za pomocą modelu en-fr przetłumacz dowolne zdanie z angielskiego na język francuski i sprawdź wagi atencji dla tego tłumaczenia