Create README.md

README.md

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+---
+language: en
+tags:
+- exbert
+license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
+---
+
+# Finetuned DistilBERT
+
+This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
+introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
+[here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does
+not make a difference between english and English.
+
+## Model description
+
+DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
+self-supervised fashion, using the BERT base model as a teacher. This model is further finetuned on the DB_PEDIA Dataset which can be found
+[here](https://huggingface.co/datasets/DeveloperOats/DBPedia_Classes). This dataset consists of 342,782 Wikipedia articles that have been cleaned and classified into hierarchical classes. 
+The classification system spans three levels, with 9 classes at the first level, 70 classes at the second level,
+and 219 classes at the third level.
+
+## Intended uses & limitations
+You can use the model to extract structured content and organizing it into taxonomic categories. 
+
+
+### How to use
+
+You can use this model directly with a pipeline:
+
+from transformers import pipeline
+import numpy as np
+
+text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."
+classifier = pipeline("text-classification", model="carbonnnnn/T2L1DISTILBERT")
+
+labeltxt = np.loadtxt("TASK2/label_vals/l1.txt", dtype="str")
+labelint = ['LABEL_0', 'LABEL_1', 'LABEL_2', 'LABEL_3', 'LABEL_4', 'LABEL_5', 'LABEL_6', 'LABEL_7', 'LABEL_8']
+
+output = classifier(text)[0]['label']
+for i in range(len(labelint)):
+    if output == labelint[i]:
+      print("Output is : " + str(labeltxt[i])) 
+
+### Limitations and bias
+
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions. It also inherits some of
+[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
+
+
+## Evaluation results
+
+
+
+### BibTeX entry and citation info
+
+
+<a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
+	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
+</a>