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https://github.com/qdrant/fastembed.git
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331207976e
* MiniLM fix * Added MiniLM to text embedding Fixed MiniLM source destination Black + isort for repo * Fixed model all-MiniLM-L6-v2 description Recomputed canonical vector for all-MiniLM-L6-v2 in test --------- Co-authored-by: d.rudenko <dimitriyrudenk@gmail.com>
151 lines
4.9 KiB
Python
151 lines
4.9 KiB
Python
# %% [markdown]
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# # 🤗 Huggingface vs ⚡ FastEmbed️
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#
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# Comparing the performance of Huggingface's 🤗 Transformers and ⚡ FastEmbed️ on a simple task on the following machine: Apple M2 Max, 32 GB RAM
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#
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# ## 📦 Imports
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#
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# Importing the necessary libraries for this comparison.
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# %%
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import time
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from typing import Callable, List, Tuple
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import matplotlib.pyplot as plt
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import torch.nn.functional as F
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from transformers import AutoModel, AutoTokenizer
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from fastembed.embedding import DefaultEmbedding
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# %% [markdown]
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# ## 📖 Data
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#
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# data is a list of strings, each string is a document.
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# %%
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documents: List[str] = [
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"Chandrayaan-3 is India's third lunar mission",
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"It aimed to land a rover on the Moon's surface - joining the US, China and Russia",
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"The mission is a follow-up to Chandrayaan-2, which had partial success",
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"Chandrayaan-3 will be launched by the Indian Space Research Organisation (ISRO)",
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"The estimated cost of the mission is around $35 million",
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"It will carry instruments to study the lunar surface and atmosphere",
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"Chandrayaan-3 landed on the Moon's surface on 23rd August 2023",
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"It consists of a lander named Vikram and a rover named Pragyan similar to Chandrayaan-2. Its propulsion module would act like an orbiter.",
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"The propulsion module carries the lander and rover configuration until the spacecraft is in a 100-kilometre (62 mi) lunar orbit",
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"The mission used GSLV Mk III rocket for its launch",
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"Chandrayaan-3 was launched from the Satish Dhawan Space Centre in Sriharikota",
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"Chandrayaan-3 was launched earlier in the year 2023",
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]
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len(documents)
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# %% [markdown]
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# ## Setting up 🤗 Huggingface
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#
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# We'll be using the [Huggingface Transformers](https://huggingface.co/transformers/) with PyTorch library to generate embeddings. We'll be using the same model across both libraries for a fair(er?) comparison.
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# %%
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class HF:
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"""
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HuggingFace Transformer implementation of FlagEmbedding
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Based on https://huggingface.co/BAAI/bge-base-en
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"""
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def __init__(self, model_id: str):
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self.model = AutoModel.from_pretrained(model_id)
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self.tokenizer = AutoTokenizer.from_pretrained(model_id)
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def embed(self, texts: List[str]):
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encoded_input = self.tokenizer(
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texts, max_length=512, padding=True, truncation=True, return_tensors="pt"
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)
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model_output = self.model(**encoded_input)
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sentence_embeddings = model_output[0][:, 0]
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sentence_embeddings = F.normalize(sentence_embeddings)
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return sentence_embeddings
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hf = HF(model_id="BAAI/bge-small-en")
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hf.embed(documents).shape
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# %% [markdown]
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# ## Setting up ⚡️FastEmbed
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#
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# Sorry, don't have a lot to set up here. We'll be using the default model, which is Flag Embedding, same as the Huggingface model.
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# %%
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embedding_model = DefaultEmbedding()
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# %% [markdown]
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# ## 📊 Comparison
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#
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# We'll be comparing the following metrics: Minimum, Maximum, Mean, across k runs. Let's write a function to do that:
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#
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# ### 🚀 Calculating Stats
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# %%
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def calculate_time_stats(
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embed_func: Callable, documents: list, k: int
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) -> Tuple[float, float, float]:
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times = []
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for _ in range(k):
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# Timing the embed_func call
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start_time = time.time()
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embed_func(documents)
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end_time = time.time()
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times.append(end_time - start_time)
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# Returning mean, max, and min time for the call
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return (sum(times) / k, max(times), min(times))
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# %%
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hf_stats = calculate_time_stats(hf.embed, documents, k=2)
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print(f"Huggingface Transformers (Average, Max, Min): {hf_stats}")
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fst_stats = calculate_time_stats(
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lambda x: list(embedding_model.embed(x)), documents, k=2
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)
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print(f"FastEmbed (Average, Max, Min): {fst_stats}")
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# %%
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def plot_character_per_second_comparison(
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hf_stats: Tuple[float, float, float],
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fst_stats: Tuple[float, float, float],
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documents: list,
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):
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# Calculating total characters in documents
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total_characters = sum(len(doc) for doc in documents)
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# Calculating characters per second for each model
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hf_chars_per_sec = total_characters / hf_stats[0] # Mean time is at index 0
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fst_chars_per_sec = total_characters / fst_stats[0]
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# Plotting the bar chart
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models = ["HF Embed (Torch)", "FastEmbed"]
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chars_per_sec = [hf_chars_per_sec, fst_chars_per_sec]
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bars = plt.bar(models, chars_per_sec, color=["#1f356c", "#dd1f4b"])
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plt.ylabel("Characters per Second")
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plt.title("Characters Processed per Second Comparison")
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# Adding the number at the top of each bar
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for bar, chars in zip(bars, chars_per_sec):
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plt.text(
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bar.get_x() + bar.get_width() / 2,
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bar.get_height(),
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f"{chars:.1f}",
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ha="center",
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va="bottom",
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color="#1f356c",
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fontsize=12,
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)
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plt.show()
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plot_character_per_second_comparison(hf_stats, fst_stats, documents)
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