Update README.md

README.md

@@ -1,92 +1,130 @@
----
-base_model: distilbert-base-cased
-datasets:
-- conll2003
-license: apache-2.0
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-tags:
-- generated_from_trainer
-model-index:
-- name: distilbert-finetuned-ner
-  results:
-  - task:
-      type: token-classification
-      name: Token Classification
-    dataset:
-      name: conll2003
-      type: conll2003
-      config: conll2003
-      split: validation
-      args: conll2003
-    metrics:
-    - type: precision
-      value: 1.0
-      name: Precision
-    - type: recall
-      value: 1.0
-      name: Recall
-    - type: f1
-      value: 1.0
-      name: F1
-    - type: accuracy
-      value: 1.0
-      name: Accuracy
----
-
-<!-- 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. -->
-
-# distilbert-finetuned-ner
-
-This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0736
-- Precision: 1.0
-- Recall: 1.0
-- F1: 1.0
-- Accuracy: 1.0
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
-| 0.0911        | 1.0   | 1756 | 0.0875          | 1.0       | 1.0    | 1.0 | 1.0      |
-| 0.0469        | 2.0   | 3512 | 0.0736          | 1.0       | 1.0    | 1.0 | 1.0      |
-| 0.0284        | 3.0   | 5268 | 0.0736          | 1.0       | 1.0    | 1.0 | 1.0      |
-
-
-### Framework versions
-
-- Transformers 4.41.2
-- Pytorch 2.3.1
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+---
+base_model: distilbert-base-cased
+datasets:
+- conll2003
+license: mit
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+tags:
+- generated_from_trainer
+model-index:
+- name: distilbert-finetuned-ner
+  results:
+  - task:
+      type: token-classification
+      name: Token Classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - type: precision
+      value: 1
+      name: Precision
+    - type: recall
+      value: 1
+      name: Recall
+    - type: f1
+      value: 1
+      name: F1
+    - type: accuracy
+      value: 1
+      name: Accuracy
+---
+
+<!-- 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. -->
+
+# distilbert-finetuned-ner
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0711
+- Precision: 1.0
+- Recall: 1.0
+- F1: 1.0
+- Accuracy: 1.0
+
+## Model description
+
+The distilbert-finetuned-ner model is designed for Named Entity Recognition (NER) tasks. It is based on the DistilBERT architecture, which is a smaller, faster, and lighter version of BERT. DistilBERT retains 97% of BERT's language understanding while being 60% faster and 40% smaller, making it efficient for deployment in production systems.
+
+## Intended Uses & Limitations
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("amanpatkar/distilbert-finetuned-ner")
+model = AutoModelForTokenClassification.from_pretrained("amanpatkar/distilbert-finetuned-ner")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Aman Patkar and I live in Gurugram, India."
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+### Intended Uses
+- Named Entity Recognition (NER): Extracting entities such as names, locations, organizations, and miscellaneous entities from text.
+- Information Extraction: Automatically identifying and classifying key information in documents.
+- Text Preprocessing: Enhancing text preprocessing for downstream tasks like sentiment analysis and text summarization.
+
+### limitations
+- Domain Specificity: The model is trained on the CoNLL-2003 dataset, which primarily consists of newswire data. Performance may degrade on text from different domains.
+- Language Limitation: This model is trained on English text. It may not perform well on text in other languages.
+- Precision in Complex Sentences: While the model performs well on standard sentences, complex sentence structures or ambiguous contexts might pose challenges.
+
+
+## Training and evaluation data
+
+The model is fine-tuned on the CoNLL-2003 dataset, a widely-used dataset for training and evaluating NER systems. The dataset includes four types of named entities: Persons (PER), Organizations (ORG), Locations (LOC), and Miscellaneous (MISC).
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity
+I-MISC | Miscellaneous entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organization right after another organization
+I-ORG |organization
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
+| 0.0908        | 1.0   | 1756 | 0.0887          | 1.0       | 1.0    | 1.0 | 1.0      |
+| 0.0467        | 2.0   | 3512 | 0.0713          | 1.0       | 1.0    | 1.0 | 1.0      |
+| 0.0276        | 3.0   | 5268 | 0.0711          | 1.0       | 1.0    | 1.0 | 1.0      |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1
+- Datasets 2.20.0
+- Tokenizers 0.19.1