Toolkit for finetuning and evaluating transformer based language models
Project description
(Framework for Adapting Representation Models)
What is it?
FARM makes cutting edge Transfer Learning for NLP simple. It is a home for all species of pretrained language models (e.g. BERT) that can be adapted to different down-stream tasks. The aim is to make it simple to perform document classification, NER and question answering, for example, using the one language model. The standardized interfaces for language models and prediction heads allow flexible extension by researchers and easy adaptation for practitioners. Additional experiment tracking and visualizations support you along the way to adapt a SOTA model to your own NLP problem and showcase it as a PoC.
- Have a look at this blog post for an introduction to Transfer Learning
or see the full documentation for more details about FARM
Core features
- Easy adaptation of pretrained language models (e.g. BERT) to your own use case
The Processor class makes it easy to define the data processing needed for your task
- Modular design of language model and prediction heads
The language model captures a core language understanding that can be shared across tasks
A prediction head uses the output of the language model to perform specific downstream tasks and can be easily tailored to your needs
Easy experiment tracking & execution
Simple deployment and visualization to showcase your PoC
Installation
The package is tested with Python 3.7.:
git clone https://github.com/deepset-ai/FARM.git cd FARM pip install -r requirements.txt pip install .
Basic Usage
1. Train a downstream model
FARM offers two modes for model training:
Option 1: Run experiment(s) from config:
from farm.experiment import run_experiment, load_experiments
experiments = load_experiments("experiments/ner/conll2003_de_config.json")
run_experiment(experiments[0])
Use cases: Training your first model, hyperparameter optimization, evaluating a language model on multiple down-stream tasks.
Option 2: Stick together your own building blocks:
# Basic building blocks for data handling
tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path=lang_model)
processor = CONLLProcessor(tokenizer=tokenizer, data_dir="../data/conll03-de", max_seq_len=128)
...
# An AdaptiveModel is the combination of a language model and one or more prediction heads
language_model = Bert.load(lang_model)
prediction_head = TokenClassificationHead(layer_dims=[768, num_labels])
model = AdaptiveModel(language_model=language_model, prediction_heads=[prediction_head], ...)
...
# Feed it to a Trainer, which keeps care of growing our model
trainer = Trainer(optimizer=optimizer, data_silo=data_silo,
epochs=n_epochs,
n_gpu=1,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device,
)
# 7. Let it grow
model = trainer.train(model)
See this tutorial for details.
Usecases: Custom datasets, language models, prediction heads …
Metrics and parameters of your model training get automatically logged via MLflow. We provide a public MLflow server for testing and learning purposes. Check it out to see your own experiment results!
2. Run Inference (API + UI)
Run
docker-compose upOpen http://localhost:3000 in your browser
One docker container exposes a REST API (localhost:5000) and another one runs a simple demo UI (localhost:3000). You can use both of them individually and mount your own models. Check out the docs for details.
Upcoming features
More pretrained models XLNet, XLM …
SOTA adaptation strategies (Adapter Modules, Discriminative Fine-tuning …)
Enabling large scale deployment for production
Additional Visualizations and statistics to explore and debug your model
Release history Release notifications | RSS feed
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