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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": [
+    "FeelWiseModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "config.FeelWiseConfig",
+    "AutoModel": "model.FeelWiseModel"
+  },
+  "d_ff": 1024,
+  "d_model": 256,
+  "dropout": 0.1,
+  "input_vocab_size": 50000,
+  "max_len": 500,
+  "model_type": "FeelWiseEmotion",
+  "n_head": 8,
+  "n_layers": 1,
+  "num_classes": 6,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2"
+}

config.py

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+from transformers import PretrainedConfig
+
+class FeelWiseConfig(PretrainedConfig):
+    model_type = "FeelWiseEmotion"
+
+    def __init__(self, d_model=256, max_len=500, input_vocab_size=50000, n_layers=1, n_head=8, d_ff=1024, num_classes=6, dropout=0.1, **kwargs):
+        super().__init__(**kwargs)
+        self.d_model = d_model
+        self.max_len = max_len
+        self.input_vocab_size = input_vocab_size
+        self.n_layers = n_layers
+        self.n_head = n_head
+        self.d_ff = d_ff
+        self.num_classes = num_classes
+        self.dropout = dropout

model.py

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+import torch.nn as nn
+import numpy as np
+import torch
+from transformers import PreTrainedModel
+from .config import FeelWiseConfig
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=500):
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        pos = np.arange(max_len)[:, np.newaxis]
+        i = np.arange(d_model)[np.newaxis, :]
+        angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
+        pos_encoding = pos * angle_rates
+        pos_encoding[:, 0::2] = np.sin(pos_encoding[:, 0::2])
+        pos_encoding[:, 1::2] = np.cos(pos_encoding[:, 1::2])
+        self.pos_encoding = torch.tensor(pos_encoding, dtype=torch.float32)
+
+    def forward(self, x):
+        x = x * np.sqrt(self.d_model)
+        x = x + self.pos_encoding[:x.size(1), :].to(x.device)
+        return x
+
+class AddNorm(nn.Module):
+    def __init__(self, d_model):
+        super(AddNorm, self).__init__()
+        self.layer_norm = nn.LayerNorm(d_model)
+
+    def forward(self, x, sub_layer_x):
+        return self.layer_norm(x + sub_layer_x)
+
+class FeedForward(nn.Module):
+    def __init__(self, d_model, d_ff):
+        super(FeedForward, self).__init__()
+        self.linear1 = nn.Linear(d_model, d_ff)
+        self.linear2 = nn.Linear(d_ff, d_model)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        return self.linear2(self.relu(self.linear1(x)))
+
+class EncoderLayer(nn.Module):
+    def __init__(self, d_model, n_head, d_ff, dropout=0.1):
+        super(EncoderLayer, self).__init__()
+        self.multi_head_attention = nn.MultiheadAttention(d_model, n_head)
+        self.add_norm1 = AddNorm(d_model)
+        self.feed_forward = FeedForward(d_model, d_ff)
+        self.add_norm2 = AddNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        sub_layer_x = self.multi_head_attention(x, x, x)[0]
+        sub_layer_x = self.dropout(sub_layer_x)
+        x = self.add_norm1(x, sub_layer_x)
+        sub_layer_x = self.feed_forward(x)
+        sub_layer_x = self.dropout(sub_layer_x)
+        x = self.add_norm2(x, sub_layer_x)
+        return x
+
+class Encoder(nn.Module):
+    def __init__(self, n_layers, d_model, max_len, input_vocab_size, n_head, d_ff, dropout=0.1):
+        super(Encoder, self).__init__()
+        self.layers = nn.ModuleList([EncoderLayer(d_model, n_head, d_ff, dropout) for _ in range(n_layers)])
+        self.embedding = nn.Embedding(input_vocab_size, d_model)
+        self.pos_encoding = PositionalEncoding(d_model, max_len)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = self.embedding(x)
+        x = self.pos_encoding(x)
+        x = self.dropout(x)
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+class FeelWiseModel(PreTrainedModel):
+    config_class = FeelWiseConfig
+    base_model_prefix = "FeelWiseEmotion"
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.encoder = Encoder(config.n_layers, config.d_model, config.max_len, config.input_vocab_size, config.n_head, config.d_ff, config.dropout)
+        self.fc = nn.Linear(config.d_model, config.num_classes)  # Final classification layer
+
+    def forward(self, input_ids):
+        x = self.encoder(input_ids)  # Include attention_mask if your encoder uses it
+        x = x.mean(dim=1)
+        logits = self.fc(x)
+        return logits

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:85bb9940a04bbbafad41f16bbee6f6fa59bd8935c8611edcbdd6790e5afed133
+size 54366880

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<cls>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<sep>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<cls>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<sep>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<cls>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 50,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "<sep>",
+  "special_tokens": [
+    "<s>",
+    "<pad>",
+    "</s>",
+    "<unk>",
+    "<cls>",
+    "<sep>",
+    "<mask>"
+  ],
+  "stride": 0,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}