#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/wVN12smEvqg?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

! pip install datasets transformers[sentencepiece]

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
classifier([
    "I've been waiting for a HuggingFace course my whole life.", 
    "I hate this so much!"
])

from transformers import AutoTokenizer

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

raw_inputs = [
    "I've been waiting for a HuggingFace course my whole life.", 
    "I hate this so much!",
]
inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="tf")
print(inputs)

from transformers import TFAutoModel

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
model = TFAutoModel.from_pretrained(checkpoint)
outputs = model(inputs)
print(outputs.last_hidden_state.shape)

from transformers import TFAutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
outputs = model(inputs)
print(outputs.logits)

import tensorflow as tf

predictions = tf.math.softmax(outputs.logits, axis=-1)
print(predictions)

model.config.id2label