Shah1st
/

mountain-ner-model

@@ -7,9 +7,14 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
-## Model Details
 ### Model Description
@@ -17,49 +22,29 @@ tags: []
 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]
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-## Uses
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 ### Direct Use
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-## Bias, Risks, and Limitations
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 ### Recommendations
@@ -79,121 +64,30 @@ Use the code below to get started with the model.
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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 ## Evaluation
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 #### Summary
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-## Environmental Impact
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-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).
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-## Technical Specifications [optional]
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 <!-- Provide a quick summary of what the model is/does. -->
+This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.
+Features:
+Fine-tuned on a custom dataset that includes sentences both with and without mountain names.
+Uses focal loss to handle class imbalance, which ensures the model focuses on correctly classifying rare mountain names.
+Token-level classification for identifying the B-MOUNTAIN, I-MOUNTAIN, and O (non-entity) labels.
+Balances training between sentences with mountains (80%) and without mountains (20%).
 ### Model Description
 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:** Oleksandr Kharytonov
+- **Model type:** BERT
+- **Language(s) (NLP):** Python
+- **License:** MIT
+- **Finetuned from model [optional]:** https://huggingface.co/dslim/bert-large-NER
+-
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/Shah1st/mountain-ner
 ### Direct Use
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+tokenizer = AutoTokenizer.from_pretrained('./saved_model')
+model = AutoModelForTokenClassification.from_pretrained('./saved_model')
+```
 ### Recommendations
 <!-- 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. -->
+"DFKI-SLT/few-nerd", "supervised"
+Filter for sentences with 'fine_ner_tags' == 24 (mountains)
 ## Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
+'eval_loss': 0.009154710918664932, "eval_precision_'O'": 0.9846412638045784, "eval_recall_'O'": 0.9890537760799295, "eval_f1_'O'": 0.9868425875022907,
+'eval_macro_f1': 0.8952192988290304, 'eval_accuracy': 0.9746226793108054
+### Testing Data, Factors & Metrics
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
+macro F1: 0.895
+Accuracy: 0.974
 #### Summary
+This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.