bert-for-sequence-classification 0.1.1

Easy fine-tuning for BERT models

bert-for-sequence-classification

Pipeline for easy fine-tuning of BERT architecture for sequence classification

Quick Start

Installation

1. Install the library

pip install bert-for-sequence-classification

2. If you want to train you model on GPU, please install pytorch version compatible with your device.

To find the version compatible with the cuda installed on your GPU, check Pytorch website. You can learn CUDA version installed on your device by typing nvidia-smi in console or !nvidia-smi in a notebook cell.

CLI Use

bert-clf-train --path_to_config <path to yaml file>

Example config file can be found here

Jupyter notebook

Example notebook can be found here

Inference mode

When using your trained model for inference it depends on how you saved your model

if path_to_state_dict in config is equal to false, then if you have the library installed:

import torch
import pandas as pd

device = torch.device("cuda" if  torch.cuda.is_available() else "cpu")

model = torch.load(
    "path_to_saved_model", map_location=device
)
    
model.eval()

df = pd.read_csv("path_to_some_df")

df["target_column"] = df["text_column"].apply(model.predict)

Otherwise:

import torch
import json
import pandas as pd
from bert_clf.src.models.BertCLF import BertCLF
from transformers import AutoModel, AutoTokenizer

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(
    pretrained_model_name_or_path="pretrained_model_name_or_path"
)
model_bert = AutoModel.from_pretrained(
    pretrained_model_name_or_path="pretrained_model_name_or_path"
).to(device)

id2label = json.load(open("path/to/saved/mapper"))  # mapper is saved with the state dict

model = BertCLF(
    pretrained_model=model_bert,
    tokenizer=tokenizer,
    id2label=id2label,
    dropout="some number",
    device=device
)

model.load_state_dict(
    torch.load(
        "path_to_state_dict", map_location=device
    ),
    strict=False
)

model.eval()

df = pd.read_csv("path_to_some_df")

df["target_column"] = df["text_column"].apply(model.predict)