Rocketknight1

import tensorflow as tf
from tensorflow_addons.layers import embeddingbag
import numpy as np
from time import perf_counter


@tf.function
def composite_run(indices, values, weights):
    with tf.GradientTape() as tape:
        tape.watch(values)

Rocketknight1

import tensorflow as tf
from tensorflow_addons.layers import embeddingbag
import numpy as np
from time import perf_counter


@tf.function
def composite_run(indices, values, weights):
    with tf.GradientTape() as tape:
        tape.watch(values)

Rocketknight1

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
import tensorflow as tf

model_name = 'bert-base-cased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

texts = ["I'm a positive example!", "I'm a negative example!"]
labels = [1, 0]

Rocketknight1

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
import tensorflow as tf

model_name = 'bert-base-cased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

texts = ["I'm a positive example!", "I'm a negative example!"]
labels = [1, 0]

Rocketknight1

from collections import Counter

words = [word.strip() for word in open("wordle-answers-alphabetical.txt").readlines() if word.strip()]
allowed_words = [word.strip() for word in open("wordle-allowed-guesses.txt").readlines() if word.strip()]
allowed_words = list(set(allowed_words) | set(words))

def compare_guess(guess, answer):
    answer = list(answer)
    output = ['X' for _ in range(len(answer))]
    for i, letter in enumerate(guess):

Rocketknight1

from collections import Counter

words = [word.strip() for word in open("wordle-answers-alphabetical.txt").readlines() if word.strip()]
allowed_words = [word.strip() for word in open("wordle-allowed-guesses.txt").readlines() if word.strip()]
allowed_words = list(set(allowed_words) | set(words))

def compare_guess(guess, answer):
    answer = list(answer)
    output = ['X' for _ in range(len(answer))]
    for i, letter in enumerate(guess):

Rocketknight1

import tensorflow as tf
from transformers import TFAutoModel, TFBertTokenizer


class EndToEndModel(tf.keras.Model):
    def __init__(self, checkpoint):
        super().__init__()
        self.tokenizer = TFBertTokenizer.from_pretrained(checkpoint)
        self.model = TFAutoModel.from_pretrained(checkpoint)
        

Rocketknight1

# This is a new feature, so make sure to install transformers from main first!

import tensorflow as tf
from transformers import TFAutoModel, TFBertTokenizer


class EndToEndModel(tf.keras.Model):
    def __init__(self, checkpoint):
        super().__init__()
        self.tokenizer = TFBertTokenizer.from_pretrained(checkpoint)

Rocketknight1

# This is a new feature, so make sure to install transformers from main first!

import tensorflow as tf
from transformers import TFAutoModel, TFBertTokenizer


class EndToEndModel(tf.keras.Model):
    def __init__(self, checkpoint):
        super().__init__()
        self.tokenizer = TFBertTokenizer.from_pretrained(checkpoint)

Rocketknight1

# This is a new feature, so make sure to update to the latest version of transformers!
# You will also need to pip install tensorflow_text

import tensorflow as tf
from transformers import TFAutoModel, TFBertTokenizer


class EndToEndModel(tf.keras.Model):
    def __init__(self, checkpoint):
        super().__init__()