File size: 2,053 Bytes
f9c91de 6c03063 ad4a924 6c03063 ad4a924 ea14799 ad4a924 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
license: apache-2.0
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
base_model:
- dslim/distilbert-NER
pipeline_tag: token-classification
---
Ir is fine-tuned [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.
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 with sentances wich contains mountains.
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
-|-
216 |2783
## Eval results
| Metric | Score |
|------------|-------|
| Loss | 0.2035|
| Precision | 0.8536|
| Recall | 0.7906|
| F1 | 0.7117|
| Accuracy | 0.7906| |