Update README.md

README.md

@@ -1,37 +1,33 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- gec
+- grammar
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- microsoft/deberta-large
+pipeline_tag: token-classification
 ---
 
 # 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]
+This model is a grammar error correction (GEC) system fine-tuned from the `microsoft/deberta-large` model, designed to detect and correct grammatical errors in English text. The model focuses on common grammatical mistakes such as verb tense, noun inflection, adjective usage, and more. It is particularly useful for language learners or applications requiring enhanced grammatical precision.
 
-<!-- Provide the basic links for the model. -->
+- **Model type:** Token classification with sequence-to-sequence correction
+- **Language(s) (NLP):** English
+- **Finetuned from model:** `microsoft/deberta-large`
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
@@ -41,37 +37,188 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+This model can be used directly for grammar error detection and correction in English texts. It's ideal for integration into writing assistants, educational software, or proofreading tools.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be fine-tuned for specific domains like academic writing, business communication, or informal text correction, ensuring high precision in context-specific grammar errors.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+This model is not suitable for non-English text, non-grammatical corrections (e.g., style, tone, or logic), or detecting complex errors beyond basic grammar structures.
+
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+The model is trained on general English corpora and may underperform with non-standard dialects (e.g Spoken language), or domain-specific jargon. Users should be cautious when applying it to such contexts, as it might introduce or overlook errors due to the limitations in its training data.
+
 
 ### 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.
+While the model provides strong general performance, users should manually review corrections, especially in specialized or creative contexts where grammar rules can be more fluid.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+Use the following code to get started with the model:
+
+```python
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import AutoConfig, AutoTokenizer
+from transformers.file_utils import ModelOutput
+from transformers.models.deberta.modeling_deberta import (
+    DebertaModel,
+    DebertaPreTrainedModel,
+)
+
+
+@dataclass
+class XGECToROutput(ModelOutput):
+    """
+    Output type of `XGECToRForTokenClassification.forward()`.
+    loss (`torch.FloatTensor`, optional)
+    logits_correction (`torch.FloatTensor`) : The correction logits for each token.
+    logits_detection (`torch.FloatTensor`) : The detection logits for each token.
+    hidden_states (`Tuple[torch.FloatTensor]`, optional)
+    attentions (`Tuple[torch.FloatTensor]`, optional)
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits_correction: torch.FloatTensor = None
+    logits_detection: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class XGECToRDeberta(DebertaPreTrainedModel):
+    """
+    This class overrides the GECToR model to include an error detection head in addition to the token classification head.
+    """
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.unk_tag_idx = config.label2id.get("@@UNKNOWN@@", None)
+
+        self.deberta = DebertaModel(config)
+
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        if self.unk_tag_idx is not None:
+            self.error_detector = nn.Linear(config.hidden_size, 3)
+        else:
+            self.error_detector = nn.Linear(config.hidden_size, 2)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.deberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits_correction = self.classifier(sequence_output)
+        logits_detection = self.error_detector(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                logits_correction.view(-1, self.num_labels), labels.view(-1)
+            )
+
+            labels_detection = torch.ones_like(labels)
+            labels_detection[labels == 0] = 0
+            labels_detection[labels == -100] = -100  # ignore padding
+            if self.unk_tag_idx is not None:
+                labels_detection[labels == self.unk_tag_idx] = 2
+                loss_detection = loss_fct(
+                    logits_detection.view(-1, 3), labels_detection.view(-1)
+                )
+            else:
+                loss_detection = loss_fct(
+                    logits_detection.view(-1, 2), labels_detection.view(-1)
+                )
+
+            loss += loss_detection
+
+        if not return_dict:
+            output = (
+                logits_correction,
+                logits_detection,
+            ) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return XGECToROutput(
+            loss=loss,
+            logits_correction=logits_correction,
+            logits_detection=logits_detection,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def get_input_embeddings(self):
+        return self.deberta.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.deberta.set_input_embeddings(value)
+
+
+config = AutoConfig.from_pretrained("manred1997/deberta-v3-large-lemon-spell_5k")
+tokenizer = AutoTokenizer.from_pretrained("manred1997/deberta-v3-large-lemon-spell_5k")
+model = XGECToRDeberta.from_pretrained(
+    "manred1997/deberta-v3-large-lemon-spell_5k", config=config
+)
+
+
+
+config = AutoConfig.from_pretrained("manred1997/deberta-large-lemon-spell_5k")
+tokenizer = AutoTokenizer.from_pretrained("manred1997/deberta-large-lemon-spell_5k")
+model = XGECToRDeberta.from_pretrained(
+    "manred1997/deberta-large-lemon-spell_5k", config=config
+)
+
+```
 
 ## Training Details
 
@@ -79,26 +226,15 @@ Use the code below to get started with the model.
 
 <!-- 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
+We trained the model in three stages, each requiring specific datasets. Below is a description of the data used in each stage:
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+| Stage  | Dataset(s) Used | Description |
+|--------|--------|--------|
+| Stage 1| Shuffled 9 million sentences from the PIE corpus (A1 part only)     | 9 million shuffled sentences from the PIE corpus, focusing on A1-level sentences.                                      |
+| Stage 2| Shuffled combination of NUCLE, FCE, Lang8, W&I + Locness datasets   | Lang8 dataset contained 947,344 sentences, with 52.5% having different source and target sentences.                    |
+|        |                                                                    | If using a newer Lang8 dump, consider sampling.                                                                       |                                          |
+| Stage 3| Shuffled version of W&I + Locness datasets                         | Final shuffled version of the W&I + Locness datasets.                                                                 |
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
@@ -110,90 +246,14 @@ Use the code below to get started with the model.
 
 <!-- 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]
+The model was tested on the W&I + Locness test set, a standard benchmark for grammar error correction.
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+The primary evaluation metric used was F0.5, measuring the model's ability to identify and fix grammatical errors correctly.
 
 ### 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]
+F0.5 = 73.05