---
license: apache-2.0
language:
- en
pipeline_tag: sentence-similarity
datasets:
- sentence-transformers/msmarco-msmarco-distilbert-base-v3
- microsoft/ms_marco
tags:
- taylor-m1
---

# constructai/taylor-m1

---

This model is a fine-tuned version of a custom BERT-like encoder (hidden_size=384, 6 layers) trained on MS MARCO passage ranking dataset.  
It uses **triplet hard negatives** from [`sentence-transformers/msmarco-msmarco-distilbert-base-v3`](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) (Apache-2.0 license). The base MS MARCO data is subject to [Microsoft Research License](https://github.com/microsoft/MSMARCO-Passage-Ranking/blob/master/LICENSE).

The model produces **384-dimensional** embeddings (CLS token) optimized for cosine similarity.

---

# Training details

* ~22 417 920M parameters

* Vocab Size: 30 522

* **Max sequence length: 128 tokens**

* Loss: MultipleNegativesSymmetricRankingLoss (InfoNCE)

* Batch size: 128 (32 + gradient accumulation)

* Learning rate: 2e-5

* Data: ~500k triplets from MS MARCO



---

# Evaluation

On a small test set, the model achieves:

* Positive pair similarity: 0.58

* Negative pair similarity: 0.14

* Margin: 0.44

---

# Usage

**Option 1: Via custom Python package (recommended)**

Install the package directly from GitHub:

```bash
pip install git+https://github.com/PSYCHOxSPEED/constructai-taylor-model
```

**Then load the model and get embeddings:**

```python
from taylor_model import load_taylor_model, embed_texts
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model, tokenizer, _ = load_taylor_model("constructai/taylor-m1", device=device)

texts = ["What is a neural network?", "How to make pizza at home?"]
embeddings = embed_texts(model, tokenizer, texts, device=device)
print(embeddings.shape)  # (2, 384)
```

**Compute similarity between queries and documents:**

```python
from taylor_model import load_taylor_model, embed_texts
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model, tokenizer, _ = load_taylor_model("constructai/taylor-m1", device=device)

queries = [
    "What is a neural network?",
    "How to make pizza at home?"
]
documents = [
    "A neural network is a computing system inspired by biological neural networks.",
    "For pizza you need dough, tomato sauce, mozzarella cheese and toppings."
]

q_emb = embed_texts(model, tokenizer, queries, device=device)
d_emb = embed_texts(model, tokenizer, documents, device=device)

similarities = q_emb @ d_emb.T
print("Similarity matrix:")
print(similarities)
```

---

**Requirements:**

* Python ≥ 3.9
* transformers ≥ 4.30.0
* torch ≥ 2.0.0
* huggingface_hub ≥ 0.20.0
* numpy

---

# Model details

This model was **fully created by me** (Construct AI). I designed the architecture, trained a custom WordPiece tokenizer, pre‑trained the model with MLM, and fine‑tuned it on the *MS MARCO* passage ranking dataset for semantic search. No parts of this model have been taken from other pre‑trained checkpoints – it is built from scratch.

---

# License

This model is released under the **Apache 2.0** License.

---

**I apologize if this model does not show the best quality or if you are unhappy with its maximum sequence length.**

**This is my first custom model, I tried not to do everything at once.**