---
license: mit
base_model: ml6team/keyphrase-extraction-distilbert-inspec
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: keyphrase-extraction-distilbert-inspec-finetuned-ner
  results: []
---

<!-- 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. -->

# keyphrase-extraction-distilbert-inspec-finetuned-ner

This model is a fine-tuned version of [ml6team/keyphrase-extraction-distilbert-inspec](https://huggingface.co/ml6team/keyphrase-extraction-distilbert-inspec) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7236
- Precision: 0.7952
- Recall: 0.8590
- F1: 0.8259
- Accuracy: 0.7952

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.15  | 10   | 0.7251          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |
| No log        | 0.3   | 20   | 0.7239          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |
| No log        | 0.45  | 30   | 0.7239          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |
| No log        | 0.6   | 40   | 0.7236          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |
| No log        | 0.75  | 50   | 0.7241          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |
| No log        | 0.9   | 60   | 0.7238          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |
| No log        | 1.04  | 70   | 0.7241          | 0.7952    | 0.8590 | 0.8259 | 0.7952   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2