Update README.md

README.md

@@ -12,4 +12,57 @@ base_model:
 pipeline_tag: token-classification
 ---
 Ii is [DistilBERT-NER](https://huggingface.co/dslim/distilbert-NER) model with the classifier replaced to increase the number of classes from 9 to 11. Two additional classes is I-MOU and B-MOU what stands for mountine.
-New classifier inherited
+Inital new classifier inherited all weights and biases from original and add new beurons wirh weights initialized wirh xavier_uniform_
+
+#### How to use
+
+This model can be utilized with the Transformers *pipeline* for NER, similar to the BERT models.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountines")
+model = AutoModelForTokenClassification.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountines")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
+ner_results = nlp(example)
+print(ner_results)
+```
+
+
+
+## Training data
+
+This model was fine-tuned on English castom arteficial dataset. 
+
+The t. 
+
+As in the dataset, each token will be classified as one of the following classes:
+
+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
+B-MOU |Beginning of a Mountain right after another Mountain
+I-MOU |Mountain
+
+ - |Sentences |Tokens
+-|-|-
+Dataset |216 |2783
+
+
+## Eval results
+| Metric     | Score |
+|------------|-------|
+| Loss       | 0.2035|
+| Precision  | 0.8536|
+| Recall     | 0.7906|
+| F1         | 0.7117|
+| Accuracy   | 0.7906|