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README.md

@@ -10,82 +10,6 @@ metrics:
 - f1
 - recall
 model_name: GGU-CLF
-description: '
-
-  This is a simple classification model trained on a custom dataset.
-
-  It is used to classify user text into the following classes:
-
-  - 0: Greeting
-
-  - 1: Gratitude
-
-  - 2: Unknown
-
-
-  **Note**: To use this model please remember the following things
-
-  1. The model is based on BAAI/bge-m3; You need to obtain the weights of this model
-  before you can use the classifier
-
-  2. To load the model weights you need to pass the base model and tokenizer to the
-  classifiers constructor
-
-
-
-  '
-direct_use: Use this model to classify messages from natural language chats.
-out_of_scope_use: '
-
-  The model was not trained on multi-sentence samples. **You should avoid those.**
-
-
-  Oficially tested and supported languages are **english and german** any other language
-  is considered out of scope.
-
-  '
-training_data: '
-
-  This model was trained using the [philipp-zettl/GGU-xx](https://huggingface.co/dataset/philipp-zettl/GGU-xx)
-  dataset.
-
-
-  You can find it''s performance metrics under [Evaluation Results](#evaluation-results).
-
-  '
-preprocessing: "\nThe following code was used to create the data set as well as split\
-  \ the data set into training and validation sets.\n\n```python\nfrom datasets import\
-  \ load_dataset\n\nclass Dataset:\n    def __init__(self, dataset, target_names=None):\n\
-  \        self.data = list(map(lambda x: x[0], dataset))\n        self.target = list(map(lambda\
-  \ x: x[1], dataset))\n        self.target_names = target_names\n\n\nds = load_dataset('philipp-zettl/GGU-xx')\n\
-  data = Dataset([[e['sample'], e['label']] for e in ds['train']], ['greeting', 'gratitude',\
-  \ 'unknown'])\nX_train, X_test, y_train, y_test = train_test_split(data.data, data.target,\
-  \ test_size=0.2, random_state=42)\n```\n"
-get_started_code: "\n```python\nfrom transformers import AutoModel, AutoTokenizer\n\
-  \nbase = AutoModel.from_pretrained('BAAI/bge-m3')\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-m3')\n\
-  \nmodel = EmbClf.from_pretrained(\"philipp-zettl/GGU-xx\", base_model=base.to(torch.float16),\
-  \ tokenizer=tokenizer).to('cuda').to(torch.float16)\n\nmodel([\n    'Hi was geht?',\n\
-  \    'Greetings, friendo!',\n    'I highly appreciate this gesture.',\n    'Merci\
-  \ beaucoup, nous espérons que tout se passera bien'\n]).argmax(dim=1)\n```\n"
-model_examination: "\nYou can find the initial implementation of the classification\
-  \ model here:\n\n```python\nfrom huggingface_hub import PyTorchModelHubMixin\nimport\
-  \ torch\nimport torch.nn as nn\n\nclass EmbClf(nn.Module, PyTorchModelHubMixin):\n\
-  \    def __init__(self, base_model, tokenizer, num_classes, dropout=0.0, l2_reg=0.01,\
-  \ device=None):\n        super().__init__()\n\n        self.tokenizer = tokenizer\n\
-  \        self.base = base_model\n        self.fc = nn.Linear(base.config.hidden_size,\
-  \ num_classes)\n        self.do = nn.Dropout(dropout)\n        self.device = device\n\
-  \        self.l2_reg = l2_reg\n\n    def forward(self, X):\n        encoding = self.tokenizer(X,\
-  \ return_tensors='pt', padding=True, truncation=True).to(self.device)\n        input_ids\
-  \ = encoding['input_ids']\n        attention_mask = encoding['attention_mask']\n\
-  \        emb = self.base(\n            input_ids,\n            attention_mask=attention_mask,\n\
-  \            return_dict=True,\n            output_hidden_states=True\n        ).last_hidden_state[:,\
-  \ 0, :]\n        return self.fc(self.do(emb))\n\n    def train(self, set_val=True):\n\
-  \        self.base.train(False)\n        for param in self.base.parameters():\n\
-  \            param.requires_grad = False\n        for param in self.fc.parameters():\n\
-  \            param.requires_grad = set_val\n\n    def get_l2_loss(self):\n     \
-  \   l2_loss = torch.tensor(0.).to('cuda')\n        for param in self.parameters():\n\
-  \            if param.requires_grad:\n                l2_loss += torch.norm(param,\
-  \ 2)\n        return self.l2_reg * l2_loss\n```\n"
 pipeline_tag: text-classification
 widget:
 - name: test1
@@ -105,6 +29,18 @@ widget:
 <!-- Provide a longer summary of what this model is. -->
 
 
+This is a simple classification model trained on a custom dataset.
+It is used to classify user text into the following classes:
+- 0: Greeting
+- 1: Gratitude
+- 2: Unknown
+
+**Note**: To use this model please remember the following things
+1. The model is based on BAAI/bge-m3; You need to obtain the weights of this model before you can use the classifier
+2. To load the model weights you need to pass the base model and tokenizer to the classifiers constructor
+
+
+
 
 - **Developed by:** [philipp-zettl](https://huggingface.co/philipp-zettl/)
 - **Funded by [optional]:** [More Information Needed]