Update README.md
Browse files
README.md
CHANGED
@@ -5,4 +5,41 @@ datasets:
|
|
5 |
- hate_speech_offensive
|
6 |
tags:
|
7 |
- finance
|
8 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
5 |
- hate_speech_offensive
|
6 |
tags:
|
7 |
- finance
|
8 |
+
language:
|
9 |
+
- en
|
10 |
+
---
|
11 |
+
# BERT base model (uncased)
|
12 |
+
|
13 |
+
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
|
14 |
+
[this paper](https://arxiv.org/abs/1810.04805) and first released in
|
15 |
+
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
|
16 |
+
between english and English.
|
17 |
+
|
18 |
+
## Model Details
|
19 |
+
|
20 |
+
### Model Description
|
21 |
+
|
22 |
+
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
|
23 |
+
was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
|
24 |
+
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
|
25 |
+
was pretrained with two objectives:
|
26 |
+
|
27 |
+
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
|
28 |
+
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
|
29 |
+
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
|
30 |
+
GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
|
31 |
+
sentence.
|
32 |
+
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
|
33 |
+
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
|
34 |
+
predict if the two sentences were following each other or not.
|
35 |
+
|
36 |
+
This way, the model learns an inner representation of the English language that can then be used to extract features
|
37 |
+
useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
|
38 |
+
classifier using the features produced by the BERT model as inputs.
|
39 |
+
|
40 |
+
- **Developed by:** [Jeswin MS, Venkatesh R, Kushal S Ballari]
|
41 |
+
- **Model type:** [Intent Classification]
|
42 |
+
- **Language(s) (NLP):** [English]
|
43 |
+
- **License:** []
|
44 |
+
- **Finetuned from model [optional]:** [Bert-base-uncased]
|
45 |
+
|