section2_pt.ipynb

Kernel: Python 3

Derrière le pipeline (PyTorch)

Installez la bibliothèque 🤗 Transformers pour exécuter ce notebook.

!pip install transformers[sentencepiece]

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="tblard/tf-allocine")
classifier(
    ["J'ai attendu un cours d'HuggingFace toute ma vie.",
     "Je déteste tellement ça !"]
)

from transformers import AutoTokenizer

checkpoint = "tblard/tf-allocine"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

raw_inputs = [
    "J'ai attendu un cours d'HuggingFace toute ma vie.",
    "Je déteste tellement ça !",
]
inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
print(inputs)

from transformers import AutoModel

checkpoint = "tblard/tf-allocine"
model = AutoModel.from_pretrained(checkpoint, from_tf=True)

outputs = model(**inputs)
print(outputs.last_hidden_state.shape)

from transformers import AutoModelForSequenceClassification

checkpoint = "tblard/tf-allocine"
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)
outputs = model(**inputs)

print(outputs.logits.shape)

print(outputs.logits)

import torch

predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
print(predictions)

model.config.id2label