Upload DisamBert

DisamBert.py

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+from collections.abc import Generator, Iterable
+from dataclasses import dataclass
+from enum import StrEnum
+from itertools import chain
+
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    AutoTokenizer,
+    ModernBertModel,
+    PreTrainedConfig,
+    PreTrainedModel,
+)
+from transformers.modeling_outputs import TokenClassifierOutput
+
+BATCH_SIZE = 16
+
+
+class ModelURI(StrEnum):
+    BASE = "answerdotai/ModernBERT-base"
+    LARGE = "answerdotai/ModernBERT-large"
+
+
+@dataclass(slots=True, frozen=True)
+class LexicalExample:
+    concept: str
+    definition: str
+
+
+@dataclass(slots=True, frozen=True)
+class PaddedBatch:
+    input_ids: torch.Tensor
+    attention_mask: torch.Tensor
+
+
+class DisamBert(PreTrainedModel):
+    def __init__(self, config: PreTrainedConfig):
+        super().__init__(config)
+        if config.init_basemodel:
+            self.BaseModel = AutoModel.from_pretrained(config.name_or_path, device_map="auto")
+            self.classifier_head = nn.UninitializedParameter()
+            self.bias = nn.UninitializedParameter()
+            self.__entities = None
+        else:
+            self.BaseModel = ModernBertModel(config)
+            self.classifier_head = nn.Parameter(
+                torch.empty((config.ontology_size, config.hidden_size))
+            )
+            self.bias = nn.Parameter(torch.empty((config.ontology_size, 1)))
+            self.__entities = pd.Series(config.entities)
+        config.init_basemodel = False
+        self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_path)
+        self.loss = nn.CrossEntropyLoss()
+        self.post_init()
+
+    @classmethod
+    def from_base(cls, base_id: ModelURI):
+        config = AutoConfig.from_pretrained(base_id)
+        config.init_basemodel = True
+        config.tokenizer_path = base_id
+        return cls(config)
+
+    def init_classifier(self, entities: Generator[LexicalExample]) -> None:
+        entity_ids = []
+        vectors = []
+        batch = []
+        n = 0
+        with self.BaseModel.device:
+            torch.cuda.empty_cache()
+            for entity in entities:
+                entity_ids.append(entity.concept)
+                batch.append(entity.definition)
+
+                n += 1
+                if n == BATCH_SIZE:
+                    tokens = self.tokenizer(batch, padding=True, return_tensors="pt")
+                    encoding = self.BaseModel(tokens["input_ids"], tokens["attention_mask"])
+                    vectors.append(encoding.last_hidden_state.detach()[:, 0])
+                    n = 0
+                    batch = []
+            if n > 0:
+                tokens = self.tokenizer(batch, padding=True, return_tensors="pt")
+                encoding = self.BaseModel(tokens["input_ids"], tokens["attention_mask"])
+                vectors.append(encoding.last_hidden_state.detach()[:, 0])
+
+            self.__entities = pd.Series(entity_ids)
+            self.config.entities = entity_ids
+            self.config.ontology_size = len(entity_ids)
+            self.classifier_head = nn.Parameter(torch.cat(vectors, dim=0))
+            self.bias = nn.Parameter(
+                torch.nn.init.normal_(
+                    torch.empty((self.config.ontology_size, 1)), 
+                    std=self.classifier_head.std().item() * np.sqrt(self.config.hidden_size)
+                )
+            )
+
+    @property
+    def entities(self) -> pd.Series:
+        if self.__entities is None and hasattr(self.config, "entities"):
+            self.__entities = pd.Series(self.config.entities)
+        return self.__entities
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        lengths: list[list[int]],
+        candidates: list[list[list[int]]],
+        labels: Iterable[list[int]] | None = None,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+    ) -> TokenClassifierOutput:
+        assert not nn.parameter.is_lazy(self.classifier_head), (
+            "Run init_classifier to initialise weights"
+        )
+        base_model_output = self.BaseModel(
+            input_ids,
+            attention_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+        token_vectors = base_model_output.last_hidden_state
+        span_vectors = torch.cat(
+            [
+                torch.vstack(
+                    [
+                        torch.sum(chunk, dim=0)
+                        for chunk in self.split(token_vectors[i], sentence_indices)
+                    ]
+                )
+                for (i, sentence_indices) in enumerate(lengths)
+            ]
+        )
+        logits = torch.einsum("ij,kj->ki", span_vectors, self.classifier_head) + self.bias
+        logits1 = logits - logits.min()
+        mask = torch.zeros_like(logits)
+        for i, concepts in enumerate(chain.from_iterable(candidates)):
+            mask[concepts, i] = torch.tensor(1.0)
+        logits2 = logits1 * mask
+        sentence_lengths = [len(sentence_indices) for sentence_indices in lengths]
+        maxlen = max(sentence_lengths)
+        split_logits = torch.split(logits2, sentence_lengths, dim=1)
+        logits3 = torch.stack(
+            [
+                self.extend_to_max_length(sentence, length, maxlen)
+                for (sentence, length) in zip(split_logits, sentence_lengths, strict=True)
+            ]
+        )
+        return TokenClassifierOutput(
+            logits=logits3,
+            loss=self.loss(logits3, labels) if labels is not None else None,
+            hidden_states=base_model_output.hidden_states if output_hidden_states else None,
+            attentions=base_model_output.attentions if output_attentions else None,
+        )
+
+    def split(self, vectors: torch.Tensor, lengths: list[int]) -> tuple[torch.Tensor, ...]:
+        maxlen = vectors.shape[0]
+        total_length = sum(lengths)
+        is_padded = total_length < maxlen
+        chunks = vectors.split((lengths + [maxlen - total_length]) if is_padded else lengths)
+        return chunks[:-1] if is_padded else chunks
+
+    def pad(self, tokens: Iterable[list[int]]) -> PaddedBatch:
+        lengths = [len(sentence) for sentence in tokens]
+        maxlen = max(lengths)
+        input_ids = torch.tensor(
+            [
+                sentence + [self.config.pad_token_id] * (maxlen - length)
+                for (sentence, length) in zip(tokens, lengths, strict=True)
+            ]
+        )
+        attention_mask = torch.vstack(
+            [torch.cat((torch.ones(length), torch.zeros(maxlen - length))) for length in lengths]
+        )
+        return PaddedBatch(input_ids, attention_mask)
+
+    def extend_to_max_length(
+        self, sentence: torch.Tensor, length: int, maxlength: int
+    ) -> torch.Tensor:
+        with self.BaseModel.device:
+            return (
+                torch.cat(
+                    [
+                        sentence,
+                        torch.zeros((self.config.ontology_size, maxlength - length)),
+                    ],
+                    dim=1,
+                )
+                if length < maxlength
+                else sentence
+            )
+
+    def pad_labels(self, labels: list[list[int]]) -> torch.Tensor:
+        unk = len(self.config.entities) - 1
+        lengths = [len(seq) for seq in labels]
+        maxlen = max(lengths)
+        with self.BaseModel.device:
+            return torch.tensor(
+                [
+                    seq + [unk] * (maxlen - length)
+                    for (seq, length) in zip(labels, lengths, strict=True)
+                ]
+            )
+
+    def tokenize(
+        self, batch: list[dict[str, str | list[int]]]
+    ) -> dict[str, torch.Tensor | list[list[int]]]:
+        all_indices = []
+        all_tokens = []
+        with self.BaseModel.device:
+            for example in batch:
+                text = example["text"]
+                span_indices = example["indices"]
+                indices = []
+                tokens = []
+                last_span = len(span_indices) - 2
+                for i, position in enumerate(span_indices[:-1]):
+                    span = text[position : span_indices[i + 1]]
+                    span_tokens = self.tokenizer([span], padding=False)["input_ids"][0]
+                    if i > 0:
+                        span_tokens = span_tokens[1:]
+                    if i < last_span:
+                        span_tokens = span_tokens[:-1]
+                    indices.append(len(span_tokens))
+                    tokens.extend(span_tokens)
+                all_indices.append(indices)
+                all_tokens.append(tokens)
+            padded = self.pad(all_tokens)
+            result = {
+                "input_ids": padded.input_ids,
+                "attention_mask": padded.attention_mask,
+                "lengths": all_indices,
+                "candidates": [example["candidates"] for example in batch],
+            }
+            if "labels" in batch[0]:
+                result["labels"] = self.pad_labels([example["labels"] for example in batch])
+            return result

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
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+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
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+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
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+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
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+
+
+#### Training Hyperparameters
+
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+
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+
+
+## Model Examination [optional]
+
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+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
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model.safetensors

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