sartajbhuvaji
/

bert-named-entity-recognition

@@ -1,199 +1,157 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- bert
+- ner
+license: apache-2.0
+datasets:
+- eriktks/conll2003
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: token-classification
+language:
+- en
+results:
+  - task:
+      type: token-classification
+      name: Token Classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: test
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8992
+      verified: true
+    - name: Recall
+      type: recall
+      value: 0.9115
+      verified: true
+    - name: F1
+      type: f1
+      value: 0.0.9053
+      verified: true
+    - name: loss
+      type: loss
+      value: 0.040937
+      verified: true
 ---
+# Model Card for Bert Named Entity Recognition
 ### Model Description
+This is a chat fine-tuned version of `google-bert/bert-base-uncased`, designed to perform Named Entity Recognition on a text sentence imput.
+- **Developed by:** [Sartaj](https://huggingface.co/sartajbhuvaji)
+- **Finetuned from model:** `google-bert/bert-base-uncased`
+- **Language(s):** English
+- **License:** apache-2.0
+- **Framework:** Hugging Face Transformers
+### Model Sources
+- **Repository:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)
+- **Paper:** [BERT-paper](https://huggingface.co/papers/1810.04805)
 ## Uses
+Model can be used to recognize Named Entities in text.
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("sartajbhuvaji/bert-named-entity-recognition")
+model = AutoModelForTokenClassification.from_pretrained("sartajbhuvaji/bert-named-entity-recognition")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
+ner_results = nlp(example)
+print(ner_results)
+```
+```json
+[
+  {
+    "end": 19,
+    "entity": "B-PER",
+    "index": 4,
+    "score": 0.99633455,
+    "start": 11,
+    "word": "wolfgang"
+  },
+  {
+    "end": 40,
+    "entity": "B-LOC",
+    "index": 9,
+    "score": 0.9987465,
+    "start": 34,
+    "word": "berlin"
+  }
+]
+```
 ## Training Details
+- **Dataset** : [eriktks/conll2003](https://huggingface.co/datasets/eriktks/conll2003)
+| 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
+- Full Model Finetune
+- Epochs : 5
+#### Training Loss Curves
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6354695712edd0ed5dc46b04/vVra4giLk3EPjXo48Sbax.png)
+## Trainer
+- global_step: 4390
+- training_loss: 0.040937909830132485
+- train_runtime: 206.3611
+- train_samples_per_second: 340.205
+- train_steps_per_second: 21.273
+- total_flos: 1702317283240608.0
+- train_loss: 0.040937909830132485
+- epoch: 5.0
 ## Evaluation
+- Precision: 0.8992
+- Recall: 0.9115
+- F1 Score: 0.9053
+### Classification Report
+| Class | Precision | Recall | F1-Score | Support |
+|---|---|---|---|---|
+| LOC | 0.91 | 0.93 | 0.92 | 1668 |
+| MISC | 0.76 | 0.81 | 0.78 | 702 |
+| ORG | 0.87 | 0.88 | 0.88 | 1661 |
+| PER | 0.98 | 0.97 | 0.97 | 1617 |
+| **Micro Avg** | 0.90 | 0.91 | 0.91 | 5648 |
+| **Macro Avg** | 0.88 | 0.90 | 0.89 | 5648 |
+| **Weighted Avg** | 0.90 | 0.91 | 0.91 | 5648 |
+- Evaluation Dataset : eriktks/conll2003