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mirth/chonky_mmbert_small_multilingual_1

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Model Card

Chonkymmbertsmallmultilingualv1

_Chonky_ is a transformer model that intelligently segments text into meaningful semantic chunks. This model can be used in the RAG systems.

🆕 _Now multilingual!_

Model Description

The model processes text and divides it into semantically coherent segments. These chunks can then be fed into embedding-based retrieval systems or language models as part of a RAG pipeline.

⚠️This model was fine-tuned on sequence of length 1024 (by default mmBERT supports sequence length up to 8192).

How to use

I've made a small python library for this model: chonky

Here is the usage:

from src.chonky import ParagraphSplitter

# on the first run it will download the transformer model
splitter = ParagraphSplitter(
  model_id="mirth/chonky_mmbert_small_multilingual_1",
  device="cpu"
)

text = (
    "Before college the two main things I worked on, outside of school, were writing and programming. "
    "I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. "
    "My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. "
    "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called 'data processing.' "
    "This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, "
    "and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — "
    "CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights."
)

for chunk in splitter(text):
  print(chunk)
  print("--")

Sample Output:

Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep
--
. The first programs I tried writing were on the IBM 1401 that our school district used for what was then called 'data processing.' This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.
--

But you can use this model using standart NER pipeline:

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_name = "mirth/chonky_mmbert_small_multilingual_1"

tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=1024)

id2label = {
    0: "O",
    1: "separator",
}
label2id = {
    "O": 0,
    "separator": 1,
}

model = AutoModelForTokenClassification.from_pretrained(
    model_name,
    num_labels=2,
    id2label=id2label,
    label2id=label2id,
)

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = (
    "Before college the two main things I worked on, outside of school, were writing and programming. "
    "I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. "
    "My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. "
    "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called 'data processing.' "
    "This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, "
    "and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — "
    "CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights."
)

pipe(text)

Sample output

[{'entity_group': 'separator',
  'score': np.float32(0.66304857),
  'word': ' deep',
  'start': 332,
  'end': 337}]

Training Data

The model was trained to split paragraphs from minipile, bookcorpus and Project Gutenberg datasets.

Metrics

Token based F1-score.

Project Gutenberg validation: | Model | de | en | es | fr | it | nl | pl | pt | ru | sv | zh | |------------------------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | chonkymmbertsmallmulti1 🆕 | _0.88 | 0.78 | 0.91 | 0.93 | 0.86 | 0.81 | 0.81 | 0.88 | 0.97 | 0.91 | 0.11 | | chonkymodernbertlarge1 | 0.53 | 0.43 | 0.48 | 0.51 | 0.56 | 0.21 | 0.65 | 0.53 | 0.87 | 0.51 | _0.33 | | chonkymodernbertbase1 | 0.42 | 0.38 | 0.34 | 0.4 | 0.33 | 0.22 | 0.41 | 0.35 | 0.27 | 0.31 | 0.26 | | chonkydistilbertbaseuncased1 | 0.19 | 0.3 | 0.17 | 0.2 | 0.18 | 0.04 | 0.27 | 0.21 | 0.22 | 0.19 | 0.15 | | Number of val tokens | 1m | 1m | 1m | 1m | 1m | 1m | 38k | 1m | 24k | 1m | 132k |

Various english datasets: | Model | bookcorpus | enjudgements | paulgraham | 20newsgroups | |------------------------------------------------|-----------------------|---------------------|------------------|----------------------| | chonkYmodernbertlarge1 | _0.79 | 0.29 | 0.69 | 0.17 | | chonkYmodernbertbase1 | 0.72 | 0.08 | 0.63 | 0.15 | | chonkYdistilbertbaseuncased1 | 0.69 | 0.05 | 0.52 | 0.15 | | chonkymmbertsmallmultilingual1 🆕 | 0.72 | 0.2 | 0.56 | 0.13 |

Hardware

Model was fine-tuned on a single H100 for a several hours

I dedicate this model in memory of my father.