MassMin
/

Multilingual-NER-tagging

@@ -1,10 +1,9 @@
 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
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 # XLM-RoBERTa Token Classification for Named Entity Recognition (NER)
 This model is a fine-tuned version of XLM-RoBERTa (xlm-roberta-base) for Named Entity Recognition (NER) tasks. It has been trained on the PAN-X subset of the XTREME dataset for  German Language . The model identifies the following entity types:
 PER: Person names
@@ -12,33 +11,19 @@ ORG: Organization names
 LOC: Location names
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 ## Model Details
-### Model Description
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-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
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-- **Paper [optional]:** [More Information Needed]
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 ## Uses
 This model is suitable for multilingual NER tasks, especially in scenarios where extracting and classifying person, organization, and location names in text across different languages is required.
@@ -47,75 +32,68 @@ Applications:
 Information extraction
 Multilingual NER tasks
 Automated text analysis for businesses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
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-[More Information Needed]
-### Downstream Use [optional]
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-Base Model: xlm-roberta-base
-Training Dataset: The model is trained on the PAN-X subset of the XTREME dataset, which includes labeled NER data for multiple languages.
-Training Framework: Hugging Face transformers library with PyTorch backend.
-Data Preprocessing: Tokenization was performed using XLM-RoBERTa tokenizer, with attention paid to aligning token labels to subword tokens.
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
 The model's performance is evaluated using the F1 score for NER. The predictions are aligned with gold-standard labels, ignoring sub-token predictions where appropriate.
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
  ## Evaluation
@@ -125,7 +103,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 import pandas as pd
-model_checkpoint = "MassMin/xlm-roberta-base-finetuned-panx-de"  # Replace with your Hugging Face model name
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -150,98 +128,13 @@ print(result)
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-### Results
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-#### Summary
-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
 # XLM-RoBERTa Token Classification for Named Entity Recognition (NER)
+### Model Description
 This model is a fine-tuned version of XLM-RoBERTa (xlm-roberta-base) for Named Entity Recognition (NER) tasks. It has been trained on the PAN-X subset of the XTREME dataset for  German Language . The model identifies the following entity types:
 PER: Person names
 LOC: Location names
+T
 ## Model Details
+-
 ## Uses
 This model is suitable for multilingual NER tasks, especially in scenarios where extracting and classifying person, organization, and location names in text across different languages is required.
 Information extraction
 Multilingual NER tasks
 Automated text analysis for businesses
+## Training Details
+Base Model: xlm-roberta-base
+Training Dataset: The model is trained on the PAN-X subset of the XTREME dataset, which includes labeled NER data for multiple languages.
+Training Framework: Hugging Face transformers library with PyTorch backend.
+Data Preprocessing: Tokenization was performed using XLM-RoBERTa tokenizer, with attention paid to aligning token labels to subword tokens.
+### Training Procedure
+Here's a brief overview of the training procedure for the XLM-RoBERTa model for NER:
+Setup Environment:
+Clone the repository and set up dependencies.
+Import necessary libraries and modules.
+Load Data:
+Load the PAN-X subset from the XTREME dataset.
+Shuffle and sample data subsets for training and evaluation.
+Data Preparation:
+Convert raw dataset into a format suitable for token classification.
+Define a mapping for entity tags and apply tokenization.
+Align NER tags with tokenized inputs.
+Define Model:
+Initialize the XLM-RoBERTa model for token classification.
+Configure the model with the number of labels based on the dataset.
+Setup Training Arguments:
+Define hyperparameters such as learning rate, batch size, number of epochs, and evaluation strategy.
+Configure logging and checkpointing.
+Initialize Trainer:
+Create a Trainer instance with the model, training arguments, datasets, and data collator.
+Specify evaluation metrics to monitor performance.
+Train the Model:
+Start the training process using the Trainer.
+Monitor training progress and metrics.
+Evaluation and Results:
+Evaluate the model on the validation set.
+Compute metrics like F1 score for performance assessment.
+Save and Push Model:
+Save the fine-tuned model locally or push to a model hub for sharing and further use.
 #### Training Hyperparameters
 The model's performance is evaluated using the F1 score for NER. The predictions are aligned with gold-standard labels, ignoring sub-token predictions where appropriate.
  ## Evaluation
 from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 import pandas as pd
+model_checkpoint = "MassMin/Multilingual-NER-tagging"
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#### Testing Data
+	0	1	2	3	4	5	6	7	8	9	10	11
+Tokens	2.000	Einwohnern	an	der	Danziger	Bucht	in	der	polnischen	Woiwodschaft	Pommern	.
+Tags	O	O	O	O	B-LOC	I-LOC	O	O	B-LOC	B-LOC	I-LOC	O