syubraj/espanyol_bert_based_ner

README.md

@@ -7,18 +7,14 @@ tags:
 model-index:
 - name: fine_tune_bert_output
   results: []
-language:
-- es
-metrics:
-- precision
-- recall
-- f1
-pipeline_tag: token-classification
 ---
 
-# espanyol_bert_based_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. -->
 
-This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an [wikiann](https://huggingface.co/datasets/unimelb-nlp/wikiann) dataset.
+# fine_tune_bert_output
+
+This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.3320
 - Overall Precision: 0.9051
@@ -28,63 +24,19 @@ It achieves the following results on the evaluation set:
 - Loc F1: 0.9190
 - Org F1: 0.8663
 - Per F1: 0.9367
-## Labels
-The following table represents the labels used by the model along with their corresponding indices:
-
-| Index | Label   |
-|-------|---------|
-| 0     | O       |
-| 1     | B-PER   |
-| 2     | I-PER   |
-| 3     | B-ORG   |
-| 4     | I-ORG   |
-| 5     | B-LOC   |
-| 6     | I-LOC   |
-### Label Descriptions
-- **O**: Outside of a named entity.
-- **B-PER**: Beginning of a person's name.
-- **I-PER**: Inside a person's name.
-- **B-ORG**: Beginning of an organization's name.
-- **I-ORG**: Inside an organization's name.
-- **B-LOC**: Beginning of a location name.
-- **I-LOC**: Inside a location name.
-
-## Intended Use
-- Text analysis for structured information extraction.
-- Applications like document parsing, chatbots, or customer service systems.
-## Inference Example
-
-```python
-from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
-
-# Load the model and tokenizer
-model_name = "syubraj/espanyol_bert_based_ner"
-model = AutoModelForTokenClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# The custom label mapping
-custom_label_mapping = {
-    0: 'O',        
-    1: 'B-PER',    
-    2: 'I-PER',    
-    3: 'B-ORG',    
-    4: 'I-ORG',    
-    5: 'B-LOC',    
-    6: 'I-LOC',    
-}
-ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
-
-# Sample Spanish text
-text = "Donald trabaja en Twitter"
-
-raw_results = ner_pipeline(text)
-
-for entity in raw_results:
-    label_index = int(entity['entity'].split('_')[1])
-    entity_label = custom_label_mapping.get(label_index, "UNKNOWN")
-    print(f"Word: {entity['word']}, Label: {entity_label}")
-
-```
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
 ## Training procedure
 
 ### Training hyperparameters
@@ -121,4 +73,4 @@ The following hyperparameters were used during training:
 - Transformers 4.44.2
 - Pytorch 2.4.1+cu121
 - Datasets 3.2.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1