Upload RevePositionBank

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": [
+    "RevePositionBank"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_bank.RevePositionBankConfig",
+    "AutoModel": "position_bank.RevePositionBank"
+  },
+  "dtype": "float32",
+  "model_type": "reve-position-bank",
+  "position_names": [
+    "A1",
+    "A2",
+    "C3",
+    "C4",
+    "CZ",
+    "F3",
+    "F4",
+    "F7",
+    "F8",
+    "FP1",
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+    "O1",
+    "O2",
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+    "P4",
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+    "P7",
+    "P8",
+    "Pz",
+    "T7",
+    "T8",
+    "C1",
+    "C2",
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+    "C6",
+    "CP1",
+    "CP2",
+    "CP3",
+    "CP4",
+    "CPz",
+    "FC1",
+    "FC2",
+    "FC3",
+    "FC4",
+    "FCz",
+    "P1",
+    "P2",
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+    "AFz",
+    "P5",
+    "P6",
+    "PO3",
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+    "AF8",
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+    "CP6",
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+    "FT8",
+    "Fp1",
+    "Fp2",
+    "Iz",
+    "Oz",
+    "P10",
+    "P9",
+    "PO7",
+    "PO8",
+    "TP7",
+    "TP8",
+    "F10",
+    "F9",
+    "FT10",
+    "FT9",
+    "FTT10h",
+    "FTT9h",
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+    "PO9",
+    "TP10",
+    "TP9",
+    "TPP10h",
+    "TPP8h",
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+    "CCP1h",
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+    "CCP4h",
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+    "C7",
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+    "Cp1",
+    "Cp2",
+    "Cp5",
+    "Cp6",
+    "Fc1",
+    "Fc2",
+    "Fc5",
+    "Fc6",
+    "biosemi128_A1",
+    "biosemi128_A2",
+    "biosemi128_A3",
+    "biosemi128_A4",
+    "biosemi128_A5",
+    "biosemi128_A6",
+    "biosemi128_A7",
+    "biosemi128_A8",
+    "biosemi128_A9",
+    "biosemi128_A10",
+    "biosemi128_A11",
+    "biosemi128_A12",
+    "biosemi128_A13",
+    "biosemi128_A14",
+    "biosemi128_A15",
+    "biosemi128_A16",
+    "biosemi128_A17",
+    "biosemi128_A18",
+    "biosemi128_A19",
+    "biosemi128_A20",
+    "biosemi128_A21",
+    "biosemi128_A22",
+    "biosemi128_A23",
+    "biosemi128_A24",
+    "biosemi128_A25",
+    "biosemi128_A26",
+    "biosemi128_A27",
+    "biosemi128_A28",
+    "biosemi128_A29",
+    "biosemi128_A30",
+    "biosemi128_A31",
+    "biosemi128_A32",
+    "biosemi128_B1",
+    "biosemi128_B2",
+    "biosemi128_B3",
+    "biosemi128_B4",
+    "biosemi128_B5",
+    "biosemi128_B6",
+    "biosemi128_B7",
+    "biosemi128_B8",
+    "biosemi128_B9",
+    "biosemi128_B10",
+    "biosemi128_B11",
+    "biosemi128_B12",
+    "biosemi128_B13",
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+    "biosemi128_B15",
+    "biosemi128_B16",
+    "biosemi128_B17",
+    "biosemi128_B18",
+    "biosemi128_B19",
+    "biosemi128_B20",
+    "biosemi128_B21",
+    "biosemi128_B22",
+    "biosemi128_B23",
+    "biosemi128_B24",
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+    "biosemi128_B29",
+    "biosemi128_B30",
+    "biosemi128_B31",
+    "biosemi128_B32",
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+    "biosemi128_C2",
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+    "biosemi128_C11",
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+    "biosemi128_C13",
+    "biosemi128_C14",
+    "biosemi128_C15",
+    "biosemi128_C16",
+    "biosemi128_C17",
+    "biosemi128_C18",
+    "biosemi128_C19",
+    "biosemi128_C20",
+    "biosemi128_C21",
+    "biosemi128_C22",
+    "biosemi128_C23",
+    "biosemi128_C24",
+    "biosemi128_C25",
+    "biosemi128_C26",
+    "biosemi128_C27",
+    "biosemi128_C28",
+    "biosemi128_C29",
+    "biosemi128_C30",
+    "biosemi128_C31",
+    "biosemi128_C32",
+    "biosemi128_D1",
+    "biosemi128_D2",
+    "biosemi128_D3",
+    "biosemi128_D4",
+    "biosemi128_D5",
+    "biosemi128_D6",
+    "biosemi128_D7",
+    "biosemi128_D8",
+    "biosemi128_D9",
+    "biosemi128_D10",
+    "biosemi128_D11",
+    "biosemi128_D12",
+    "biosemi128_D13",
+    "biosemi128_D14",
+    "biosemi128_D15",
+    "biosemi128_D16",
+    "biosemi128_D17",
+    "biosemi128_D18",
+    "biosemi128_D19",
+    "biosemi128_D20",
+    "biosemi128_D21",
+    "biosemi128_D22",
+    "biosemi128_D23",
+    "biosemi128_D24",
+    "biosemi128_D25",
+    "biosemi128_D26",
+    "biosemi128_D27",
+    "biosemi128_D28",
+    "biosemi128_D29",
+    "biosemi128_D30",
+    "biosemi128_D31",
+    "biosemi128_D32"
+  ],
+  "transformers_version": "4.57.1"
+}

configuration_bank.py

@@ -0,0 +1,9 @@
+from transformers import PretrainedConfig
+
+
+class RevePositionBankConfig(PretrainedConfig):
+    model_type = "reve-position-bank"
+
+    def __init__(self, position_names: list[str] = [], **kwargs):
+        super().__init__(**kwargs)
+        self.position_names = position_names

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b793820b9df0998667deb6c8ce2dbb86b38221d5165c1844bc4971941b13f13
+size 6628

position_bank.py

@@ -0,0 +1,28 @@
+import torch
+from transformers import PreTrainedModel
+
+from .configuration_bank import RevePositionBankConfig
+
+
+class RevePositionBank(PreTrainedModel):
+    config_class = RevePositionBankConfig
+
+    def __init__(self, config: RevePositionBankConfig):
+        super().__init__(config)
+
+        self.position_names = config.position_names
+        self.mapping = {name: i for i, name in enumerate(self.position_names)}
+        self.register_buffer("embedding", torch.randn(len(self.position_names), 3))
+
+    def forward(self, channel_names: list[str]):
+        indices = [self.mapping[q] for q in channel_names if q in self.mapping]
+
+        if len(indices) < len(channel_names):
+            print(f"Found {len(indices)} positions out of {len(channel_names)} channels")
+
+        indices = torch.tensor(indices, device=self.embedding.device)
+
+        return self.embedding[indices]
+
+    def get_all_positions(self):
+        return self.position_names