Unattended Lightweight Text Classifiers with LLM Embeddings
- Suitable for limited-memory CPU execution: Use efficient distilled models such as
deepset/tinyroberta-6l-768dfor embedding generation. - Logistic Regression and Nearest Centroid classification: Bypass the need for fine-tuning by utilizing LLM embeddings to efficiently categorize texts using either logistic regression or the nearest centroid through cosine similarity.
- Efficient Parallel Execution: Run hundreds of classifiers concurrently with minimal overhead by sharing the same model for embedding generation.
pip install -U fastcYou can train a text classifier with just a few lines of code:
from fastc import Fastc
tuples = [
("I just got a promotion! Feeling fantastic.", 'positive'),
("Today was terrible. I lost my wallet and missed the bus.", 'negative'),
("I had a great time with my friends at the party.", 'positive'),
("I'm so frustrated with the traffic jam this morning.", 'negative'),
("My vacation was wonderful and relaxing.", 'positive'),
("I didn't get any sleep last night because of the noise.", 'negative'),
("I'm so excited for the concert tonight!", 'positive'),
("I'm disappointed with the service at the restaurant.", 'negative'),
("The weather is beautiful and I enjoyed my walk.", 'positive'),
("I had a bad day. Nothing went right.", 'negative'),
("I'm thrilled to announce that we are expecting a baby!", 'positive'),
("I feel so lonely and sad today.", 'negative'),
("My team won the championship! We are the champions.", 'positive'),
("I can't stand my job anymore, it's so stressful.", 'negative'),
("I love spending time with my family during the holidays.", 'positive'),
("My computer crashed and I lost all my work.", 'negative'),
("I'm proud of my achievements this year.", 'positive'),
("I'm exhausted and overwhelmed with everything.", 'positive'),
]model = Fastc(
embeddings_model='microsoft/deberta-base',
kernel=Kernels.NEAREST_CENTROID,
)
model.load_dataset(tuples)
model.train()from fastc import Kernels
model = Fastc(
embeddings_model='microsoft/deberta-base',
kernel=Kernels.LOGISTIC_REGRESSION,
# cross_validation_splits=5,
# cross_validation_repeats=3,
# iterations=100,
# parameters={...},
# seed=1984,
)
model.load_dataset(tuples)
model.train()The implemented pooling strategies are:
MEAN(default)MEAN_MASKEDMAXMAX_MASKEDCLSSUMATTENTION_WEIGHTED
from fastc import Pooling
model = Fastc(
embeddings_model='microsoft/deberta-base',
pooling=Pooling.MEAN_MASKED,
)
model.load_dataset(tuples)
model.train()You can use instruct templates with instruct models such as intfloat/multilingual-e5-large-instruct. Other models may also improve in performance by using templates, even if they were not explicitly trained with them.
from fastc import ModelTemplates, Fastc, Template
# template_text = 'Instruct: {instruction}\nQuery: {text}'
template_text = ModelTemplates.E5_INSTRUCT
model = Fastc(
embeddings_model='intfloat/multilingual-e5-large-instruct',
template=Template(
template_text,
instruction='Classify as positive or negative'
),
)After training, you can save the model for future use:
model.save_model('./sentiment-classifier/')Important
Log in to HuggingFace first with huggingface-cli login
model.push_to_hub(
'braindao/sentiment-classifier',
tags=['sentiment-analysis'],
languages=['multilingual'],
private=False,
)You can load a pre-trained model either from a directory or from HuggingFace:
# From a directory
model = Fastc('./sentiment-classifier/')
# From HuggingFace
model = Fastc('braindao/sentiment-classifier')sentences = [
'I am feeling well.',
'I am in pain.',
]
# Single prediction
scores = model.predict_one(sentences[0])
print(scores['label'])
# Batch predictions
scores_list = model.predict(sentences)
for scores in scores_list:
print(scores['label'])To launch the dockerized inference server, use the following script:
./server/scripts/start-docker.shAlternatively, on the host machine:
./server/scripts/start-server.shIn both cases, an HTTP API will be available, listening on the fastc-server hashport 53256.
To classify text, use POST / with a JSON payload such as:
{
"model": "braindao/tinyroberta-6l-768d-language-identifier-en-es-ko-zh-fastc-lr",
"text": "오늘 저녁에 친구들과 함께 pizza를 먹을 거예요."
}Response:
{
"label": "ko",
"scores": {
"en": 1.0146501463135055e-08,
"es": 6.806091549848057e-09,
"ko": 0.9999852640487916,
"zh": 1.471899861513275e-05
}
}To check the fastc version, use GET /version:
Response:
{
"version": "2.2407.0"
}