Update README.md

README.md

@@ -12,57 +12,98 @@ model-index:
 - name: distilbert-NER
   results: []
 ---
+# distilbert-NER
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+## Model description
 
-# distilbert-NER
+**distilbert-NER** is the fine-tuned version of **DistilBERT**, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of **Named Entity Recognition (NER)**.
 
-This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0710
-- Precision: 0.9202
-- Recall: 0.9232
-- F1: 0.9217
-- Accuracy: 0.9810
+This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model, distilbert-NER demonstrates a robust performance in NER tasks, balancing between size, speed, and accuracy.
 
-## Model description
+The model was fine-tuned on the English version of the [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset, which is widely recognized for its comprehensive and diverse range of entity types.
 
-More information needed
+### Available NER models 
+| Model Name | Description | Parameters |
+|-------------------|-------------|------------------|
+| [bert-base-NER](https://huggingface.co/dslim/bert-base-NER) | Fine-tuned BERT-base model for NER - balanced performance | 110M |
+| [distilbert-NER](https://huggingface.co/dslim/distilbert-NER) | Fine-tuned DistilBERT - smaller, faster, lighter than BERT | 66M |
 
 ## Intended uses & limitations
 
-More information needed
+#### How to use
 
-## Training and evaluation data
+This model can be utilized with the Transformers *pipeline* for NER, similar to the BERT models.
 
-More information needed
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
-## Training procedure
+tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER")
+model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
 
-### Training hyperparameters
+ner_results = nlp(example)
+print(ner_results)
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
+#### Limitations and bias
 
-### Training results
+The performance of distilbert-NER is linked to its training on the CoNLL-2003 dataset. Therefore, it might show limited effectiveness on text data that significantly differs from this training set. Users should be aware of potential biases inherent in the training data and the possibility of entity misclassification in complex sentences.
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2748        | 1.0   | 878  | 0.0959          | 0.8886    | 0.8976 | 0.8931 | 0.9739   |
-| 0.0635        | 2.0   | 1756 | 0.0721          | 0.9199    | 0.9228 | 0.9213 | 0.9805   |
-| 0.0411        | 3.0   | 2634 | 0.0710          | 0.9202    | 0.9232 | 0.9217 | 0.9810   |
+## Training data
 
+The model was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset, known for its effectiveness in training NER models.
 
-### Framework versions
+## Training procedure
 
-- Transformers 4.35.2
-- Pytorch 2.1.0+cu121
-- Datasets 2.16.1
-- Tokenizers 0.15.1
+The training details, including hardware specifications, aren't specified. However, the model's training followed the best practices suitable for distilbert models, aiming at an efficient balance between learning efficiency and model accuracy.
+
+## Eval results
+| Metric     | Score |
+|------------|-------|
+| Loss       | 0.0710|
+| Precision  | 0.9202|
+| Recall     | 0.9232|
+| F1         | 0.9217|
+| Accuracy   | 0.9810|
+
+The training and validation losses demonstrate a decrease over epochs, signaling effective learning. The precision, recall, and F1 scores are competitive, showcasing the model's robustness in NER tasks.
+
+### BibTeX entry and citation info
+
+For DistilBERT:
+
+```
+@article{sanh2019distilbert,
+  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
+  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
+  journal={arXiv preprint arXiv:1910.01108},
+  year={2019}
+}
+```
+
+For the underlying BERT model:
+
+```
+@article{DBLP:journals/corr/abs-1810-04805,
+  author    = {Jacob Devlin and
+               Ming{-}Wei Chang and
+               Kenton Lee and
+               Kristina Toutanova},
+  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
+               Understanding},
+  journal   = {CoRR},
+  volume    = {abs/1810.04805},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1810.04805},
+  archivePrefix = {arXiv},
+  eprint    = {1810.04805},
+  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
+  bibsource = {db
+
+lp computer science bibliography, https://dblp.org}
+}
+```