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| import os | |
| os.system('pip install tensorflow') | |
| import json | |
| import numpy as np | |
| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from huggingface_hub.keras_mixin import from_pretrained_keras | |
| class CustomNonPaddingTokenLoss(keras.losses.Loss): | |
| def __init__(self, name="custom_ner_loss"): | |
| super().__init__(name=name) | |
| def call(self, y_true, y_pred): | |
| loss_fn = keras.losses.SparseCategoricalCrossentropy( | |
| from_logits=True, reduction=keras.losses.Reduction.NONE | |
| ) | |
| loss = loss_fn(y_true, y_pred) | |
| mask = tf.cast((y_true > 0), dtype=tf.float32) | |
| loss = loss * mask | |
| return tf.reduce_sum(loss) / tf.reduce_sum(mask) | |
| def lowercase_and_convert_to_ids(tokens): | |
| tokens = tf.strings.lower(tokens) | |
| return lookup_layer(tokens) | |
| def tokenize_and_convert_to_ids(text): | |
| tokens = text.split() | |
| return lowercase_and_convert_to_ids(tokens) | |
| def ner_tagging(text_1): | |
| with open('mapping.json','r') as f: | |
| mapping = json.load(f) | |
| ner_model = from_pretrained_keras("keras-io/ner-with-transformers", | |
| custom_objects={'CustomNonPaddingTokenLoss':CustomNonPaddingTokenLoss}, | |
| compile=False) | |
| sample_input = tokenize_and_convert_to_ids(text_1) | |
| sample_input = tf.reshape(sample_input, shape=[1, -1]) | |
| output = ner_model.predict(sample_input) | |
| prediction = np.argmax(output, axis=-1)[0] | |
| prediction = [mapping[str(i)] for i in prediction] | |
| text_2 = text_1.split(" ") | |
| output = [] | |
| for w in range(len(text_2)): | |
| if prediction[w] != "O": | |
| output.extend([(text_2[w], prediction[w]), (" ", None)]) | |
| else: | |
| output.extend([(text_2[w], None), (" ", None)]) | |
| return output | |
| text_1 = gr.inputs.Textbox(lines=5) | |
| ner_tag = gr.outputs.Textbox() | |
| with open("vocab.json",'r') as f: | |
| vocab = json.load(f) | |
| lookup_layer = keras.layers.StringLookup(vocabulary=vocab['tokens']) | |
| iface = gr.Interface(ner_tagging, | |
| inputs=text_1,outputs=['highlight'], examples=[['EU rejects German call to boycott British lamb .'], | |
| ["He said further scientific study was required and if it was found that action was needed it should be taken by the European Union ."]], title="Named Entity Recognition with Transformers", | |
| description = "Named Entity Recognition with Transformers on CoNLL2003 Dataset", | |
| article = "Author: <a href=\"https://huggingface.co/reichenbach\">Rishav Chandra Varma</a>") | |
| iface.launch() |