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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]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## 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. -->
+
+[More Information Needed]
+
+### 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]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## 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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "MedTSModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_medts.MedTSConfig",
+    "AutoModel": "modeling_medts.MedTSModel"
+  },
+  "block_size": 32,
+  "dropout": 0.1,
+  "model_type": "MedTS",
+  "n_embd": 512,
+  "n_head": 2,
+  "n_layer": 2,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "vocab_size": 4
+}

configuration_medts.py

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+from transformers import PretrainedConfig
+
+
+class MedTSConfig(PretrainedConfig):
+    model_type = "MedTS"
+
+    def __init__(
+        self,
+        vocab_size = 4,
+        n_embd = 128,
+        block_size=32,
+        n_layer=2,
+        n_head=2,
+        dropout=0.1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.block_size = block_size
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.dropout = dropout
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef3fe4e70562e0b33085b10f917213dbf96230cd6f5ddb55d17da512c2e3b76a
+size 25250512

modeling_medts.py

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+from transformers import PreTrainedModel
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from .configuration_medts import MedTSConfig
+
+
+class FeedFoward(nn.Module):
+    """ a simple linear layer followed by a non-linearity """
+    def __init__(self, n_embd, dropout):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Head(nn.Module):
+    """ one head of self-attention """
+    def __init__(self, head_size, n_embd, block_size):
+        super().__init__()
+        self.key = nn.Linear(n_embd, head_size, bias=False)
+        self.query = nn.Linear(n_embd, head_size, bias=False)
+        self.value = nn.Linear(n_embd, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+    def forward(self, x):
+        # input of size (batch, time-step, channels)
+        # output of size (batch, time-step, head size)
+        B,T,C = x.shape
+        k = self.key(x)   # (B,T,hs)
+        q = self.query(x) # (B,T,hs)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
+        wei = F.softmax(wei, dim=-1) # (B, T, T)
+        # perform the weighted aggregation of the values
+        v = self.value(x) # (B,T,hs)
+        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
+        return out
+
+class MultiHeadAttention(nn.Module):
+    """ multiple heads of self-attention in parallel """
+    def __init__(self, num_heads, head_size, n_embd, dropout, block_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size, n_embd, block_size) for _ in range(num_heads)])
+        self.proj = nn.Linear(head_size * num_heads, n_embd)
+        self.dropout = nn.Dropout(dropout)
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.dropout(self.proj(out))
+        return out
+
+class Block(nn.Module):
+    """ Transformer block: communication followed by computation """
+    def __init__(self, n_embd, n_head, dropout, block_size):
+        # n_embd: embedding dimension, n_head: the number of heads we'd like
+        super().__init__()
+        head_size = n_embd // n_head
+        self.sa = MultiHeadAttention(n_head, head_size, n_embd, dropout, block_size)
+        self.ffwd = FeedFoward(n_embd, dropout)
+        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+
+class PatientsTimeSeriesModel(nn.Module):
+    def __init__(self, vocab_size, n_embd, block_size, device, n_layer, n_head, dropout):
+        '''
+        args:
+        - vocab_size: int, the number of unique tokens in the vocabulary, i.e. the number of unique tests results
+        - n_embd: int, the dimension of the embedding, i.e. the number of tests results (same as vocab_size)
+        - block_size: int, the length of the context
+        '''
+        super().__init__()
+        # each token directly reads off the logits for the next token from a lookup table
+        self.position_embedding_table = nn.Embedding(block_size, vocab_size)
+        # self.sa =Head(n_embd, n_embd, block_size)
+        self.blocks = nn.Sequential(*[Block(n_embd, n_head, dropout, block_size) for _ in range(n_layer)])
+        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
+
+        self.lm_prefix = nn.Linear(vocab_size, n_embd) # linear layer to project the tokens to the embedding dimension
+        self.lm_head = nn.Linear(n_embd, vocab_size) # linear layer to project the embeddings to the vocabulary size
+        self.device = device
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, tok_emb, targets=None):
+        # tok_emb and targets are both (B,T,C) tensors
+        # where B is the batch size, T is the number of time steps and C is the number of tests results
+        B, T, C = tok_emb.shape
+        pos_emb = self.position_embedding_table(torch.arange(T, device=self.device)) # (T,Vocab_size)
+        x = tok_emb + pos_emb # (B,T,Vocab_size)
+        x = self.lm_prefix(x) # (B,T,C)
+        x = self.blocks(x) # (B,T,C)
+        x = self.ln_f(x) # (B,T,C)
+        logits = self.lm_head(x) # (B,T,vocab_size)
+        if targets is None:
+            return {"logits": logits}
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B*T, C)
+            targets = targets.view(B*T, C)
+            # TODO: Add padding mask to the loss computation
+            # loss = F.mse_loss(logits, targets)
+            loss = self.mse_loss(logits, targets, reduction="mean")
+        return {"logits": logits, "loss": loss}
+
+    def mse_loss(self, out, target, reduction):
+        mask = (target == 0)
+        loss = (out[~mask]-target[~mask])**2
+        if reduction == "mean":
+            return loss.mean()
+        elif reduction == "None":
+            return loss
+
+
+class MedTSModel(PreTrainedModel):
+    config_class = MedTSConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        
+        self.model = PatientsTimeSeriesModel(
+            vocab_size=config.vocab_size,
+            n_embd=config.n_embd,
+            block_size=config.block_size,
+            device= 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu',
+            n_layer=config.n_layer,
+            n_head=config.n_head,
+            dropout=config.dropout
+        )
+
+    def forward(self, tensor, targets=None):
+        return self.model(tensor, targets)