mirth
/

chonky_modernbert_base_1

@@ -1,199 +1,118 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+tags:
+- chunking
+- RAG
+license: mit
+datasets:
+- bookcorpus/bookcorpus
+language:
+- en
+base_model:
+- answerdotai/ModernBERT-base
 ---
+# Chonky modernbert base v1
+__Chonky__ is a transformer model that intelligently segments text into meaningful semantic chunks. This model can be used in the RAG systems.
+## 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.
+## How to use
+I've made a small python library for this model: [chonky](https://github.com/mirth/chonky)
+Here is the usage:
+```
+from chonky import TextSplitter
+# on the first run it will download the transformer model
+splitter = TextSplitter(
+  model_id="mirth/chonky_modernbert_base_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("--")
+# 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_modernbert_base_1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+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)
+# Output
+[
+  {'entity_group': 'separator', 'score': np.float32(0.91590524), 'word': ' stories.', 'start': 209, 'end': 218},
+  {'entity_group': 'separator', 'score': np.float32(0.6210419), 'word': ' processing."', 'start': 455, 'end': 468},
+  {'entity_group': 'separator', 'score': np.float32(0.7071036), 'word': '.', 'start': 652, 'end': 653}
+]
+```
+## Training Data
+The model was trained to split paragraphs from the bookcorpus dataset.
+## Metrics
+Token based metrics:
+| Metric   | Value |
+| -------- | ------|
+| F1       | 0.79  |
+| Precision| 0.83  |
+| Recall   | 0.75  |
+| Accuracy | 0.99  |
+## Hardware
+Model was fine-tuned on single H100 for a several hours