Reason ModernColBERT

🚀 Reason-ModernColBERT

Reason-ModernColBERTは後期相互作用モデルで、reasonir-hqデータセットで学習されています。このモデルは、推論集中型検索性能を評価することを目的としたBRIGHTベンチマークテストで優れた性能を発揮しています。Reason-ModernColBERTは、規模がこのモデルの45倍以上に達する70億パラメータのすべての既存モデルを上回り、Stack Exchange分割データでは、同じデータで学習された80億パラメータのReasonIR-8Bよりも平均してNDCG@10が2.5以上向上しています。このような優れた結果は、後期相互作用によるものであり、詳細は評価部分を参照してください。

🚀 クイックスタート

元文書でクイックスタートに関する内容が提供されていないため、このセクションは表示されません。

✨ 主な機能

• 高性能：BRIGHTベンチマークテストで、規模が70億パラメータに達するすべての既存モデルを上回り、Stack Exchange分割データではReasonIR-8Bよりも平均してNDCG@10が2.5以上向上しています。
• 後期相互作用：後期相互作用メカニズムにより、推論集中型検索の性能が向上します。

📦 インストール

まず、PyLateライブラリをインストールします。

pip install -U pylate

💻 使用例

基本的な使用法

ドキュメントのインデックス作成

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path=pylate_model_id,
)

# Step 2: Initialize the Voyager index
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

インデックスの作成とドキュメントのエンコードを毎回やり直す必要はありません。インデックスを作成してドキュメントを追加したら、それをロードすることで再利用できます。

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
)

上位k個のドキュメントを検索する

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

高度な使用法

再ランキング

ColBERTモデルを第1段階の検索パイプライン上で再ランキングのみに使用し、インデックスを構築しない場合は、rank関数を使用して、再ランキングするクエリとドキュメントを渡すだけです。

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path=pylate_model_id,
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

📚 ドキュメント

モデルの詳細

モデルの説明

モデルの出所

• ドキュメント：PyLateドキュメント
• リポジトリ：GitHub上のPyLate
• Hugging Face：Hugging Face上のPyLateモデル

完全なモデルアーキテクチャ

ColBERT(
  (0): Transformer({'max_seq_length': 127, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

学習の詳細

学習データセット

reasonir-hq

• データセット：train at 0275f82

• サイズ：100,521個の学習サンプル

• 列：query、pos、neg

• 最初の1000個のサンプルに基づく近似統計情報： | | クエリ | 正例 | 負例 | | ---- | ---- | ---- | ---- | | タイプ | 文字列 | 文字列 | 文字列 | | 詳細 |

  • 最小：38トークン
  • 平均：97.84トークン
  • 最大：128トークン
  |
  • 最小：85トークン
  • 平均：127.63トークン
  • 最大：128トークン
  |
  • 最小：81トークン
  • 平均：127.77トークン
  • 最大：128トークン
  |

• サンプル： | クエリ | 正例 | 負例 | | ---- | ---- | ---- | | Given this reasoning-intensive query, find relevant documents that could help answer the question. A researcher is analyzing a sound signal represented by the equation f(t) = 2sin(3πt) + sin(5πt) + 0.5sin(7πt). Using the Fourier transform, what are the frequencies, amplitudes, and phases of the individual sinusoidal components in the signal? | A sound signal is given by the equation f(t) = sin(2πt) + sin(4πt) + sin(6πt) where t is time in seconds. Use Fourier transform to find the frequencies, amplitudes, and phases of the individual sinusoidal components in the signal.
To find the frequencies, amplitudes, and phases of the individual sinusoidal components in the signal f(t) = sin(2πt) + sin(4πt) + sin(6πt), we can use the Fourier transform. The Fourier transform of a continuous function f(t) is given by:

F(ω) = ∫[f(t) * e^(-jωt)] dt

where F(ω) is the Fourier transform of f(t), ω is the angular frequency, and j is the imaginary unit (j^2 = -1). In this case, f(t) is already given as a sum of sinusoidal functions, so we can directly identify the frequencies, amplitudes, and phases of the individual components.

1. First component: sin(2πt)
- Frequency: The angular frequency is 2π, so the frequency is ω/(2π) = 1 Hz.
- Amplitude: The coefficient of the sine function is 1, so the amplitude is 1.
- Phase: There is no phase shi... | The Fourier transform is widely used in various fields, including engineering, physics, and data analysis. It is a powerful tool for decomposing a signal into its constituent frequencies. In music, for example, the Fourier transform can be used to analyze the frequency components of a sound wave. By applying the Fourier transform to a sound signal, one can identify the different frequencies present in the signal, as well as their relative amplitudes. This information can be useful in a variety of applications, such as sound filtering and audio processing. The Fourier transform can also be used to analyze images and other types of data. In image processing, the Fourier transform can be used to filter out noise and other unwanted features from an image. It can also be used to compress images by representing them in the frequency domain. In addition to its many practical applications, the Fourier transform also has a number of interesting theoretical properties. For example, it has been ... | | Given this reasoning-intensive query, find relevant documents that could help answer the question. A manufacturer is designing a cone-shaped container with a fixed volume of 200π cubic centimeters. The container's height is 12 centimeters, and the radius of the base is unknown. If the manufacturer wants to minimize the surface area of the container while maintaining its volume, what should be the radius of the base? | A right circular cone has a radius of 6cm and a slant height of 10cm. Determine the surface area of the cone.
To find the surface area of a right circular cone, we need to calculate the area of the base and the lateral surface area, and then add them together.

The base of the cone is a circle with radius r = 6 cm. The area of the base (A_base) can be found using the formula for the area of a circle:

A_base = πr^2
A_base = π(6 cm)^2
A_base = 36π cm^2

The lateral surface area (A_lateral) can be found using the formula for the lateral surface area of a cone:

A_lateral = πrs, where r is the radius and s is the slant height.

Given that the slant height s = 10 cm, we can calculate the lateral surface area:

A_lateral = π(6 cm)(10 cm)
A_lateral = 60π cm^2

Now, we can find the total surface area (A_total) by adding the base area and the lateral surface area:

A_total = A_base + A_lateral
A_total = 36π cm^2 + 60π cm^2
A_total = 96π cm^2

The surface area of the cone is 96π cm^2. | Torus-Shaped Containers in Chemical Engineering - New Designs and ApplicationsTorus-shaped containers are commonly used in chemical engineering for storing and transporting fluids. These containers have a distinctive doughnut shape, with a central hole and a circular cross-section. In this article, we will explore the design and applications of torus-shaped containers in chemical engineering.One of the main advantages of torus-shaped containers is their high volume-to-surface-area ratio. This makes them ideal for storing large quantities of fluids while minimizing the amount of material needed for construction. Additionally, the curved shape of the container provides added strength and stability, making it less prone to rupture or leakage.The design of torus-shaped containers typically involves the use of computer-aided design (CAD) software to create detailed models of the container's geometry. Engineers can then use these models to simulate various scenarios, such as fluid flow and ... | | Given this reasoning-intensive query, find relevant documents that could help answer the question. On the xy-coordinate plane, points A and B are given as A(2, 4) and B(8, -3). Determine the coordinates of the point on line segment AB that is three times as far from A as it is from B. | On the xy co-ordinate plane, point C is (5,-2) and point D is (-1,1.5). The point on line segment CD that is twice as far from C as from D is:
Answer Choices: (A) (1,-1) (B) (1,1) (C) (2,0.25) (D) (3,0.5) (E) (3,1)
Let's think about the multi-choice question step by step.
We want the point on the line that is twice as far from C as it is from D. We can examine the x and y coordinates separately since they are independent.
*It should be noted that there are two solutions to this problem, one point between C and D, and another point with D in the middle of C and the point. We can quickly look at the answer choices and see that all the points are between C and D, therefore we can search for that point using the following method:
Taking the x-coordinate first, the distance between C and D is |(x-coordinate ofC - (x-coordinate ofD|= |5 - (-1)| = 6
The x-coordinate that is twice as far from C as it is from D (and in between C andD will be 4 units from C and 2 units from D. So the ... | The concept of midpoint is often useful in various mathematical problems, but sometimes we need to find other points that divide a line segment in a particular ratio. One common scenario is when we need to find the point that divides the line segment in the ratio of the other two points. Let's consider an example to understand this better. Suppose we have two points E(3, 4) and F(7, -2) on the xy-coordinate plane, and we want to find the point G on the line segment EF such that EG:GF = 2:5. To solve this problem, we can use the concept of section formula, which states that if a point P(x, y) divides the line segment joining the points A(x1, y1) and B(x2, y2) in the ratio m:n, then the coordinates of P are ((mx2+nx1)/(m+n), (my2+ny1)/(m+n)). Using this formula, we can find the coordinates of point G. First, we need to find the difference in x-coordinates and y-coordinates of points E and F. The difference in x-coordinates is 7 - 3 = 4, and the difference in y-coordinates is -2 - 4 = -6... |

• 損失関数：pylate.losses.cached_contrastive.CachedContrastive

学習ハイパーパラメータ

非デフォルトのハイパーパラメータ

• per_device_train_batch_size：256
• per_device_eval_batch_size：256
• learning_rate：1e-05
• bf16：True
• dataloader_num_workers：8

すべてのハイパーパラメータ

学習ログ