---
library_name: transformers
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
model-index:
- name: bslm-entity-extraction-mt5-base-include-desc-normalized-tr243k
  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. -->

# bslm-entity-extraction-mt5-base-include-desc-normalized-tr243k

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0123
- Exact Match: 67.9183
- F1 Score: 88.9881

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Exact Match | F1 Score |
|:-------------:|:------:|:-----:|:---------------:|:-----------:|:--------:|
| 0.7787        | 0.0328 | 500   | 0.5270          | 0.0         | 0.0      |
| 0.0903        | 0.0656 | 1000  | 0.0410          | 40.0656     | 75.4663  |
| 0.0428        | 0.0984 | 1500  | 0.0276          | 49.4349     | 81.9724  |
| 0.0462        | 0.1312 | 2000  | 0.0276          | 50.4375     | 81.5147  |
| 0.0304        | 0.1640 | 2500  | 0.0232          | 54.2836     | 84.0088  |
| 0.0274        | 0.1968 | 3000  | 0.0218          | 55.7237     | 84.5312  |
| 0.0251        | 0.2296 | 3500  | 0.0205          | 56.7262     | 84.8972  |
| 0.0252        | 0.2624 | 4000  | 0.0209          | 55.0492     | 84.5563  |
| 0.0236        | 0.2953 | 4500  | 0.0185          | 60.2443     | 86.0929  |
| 0.0221        | 0.3281 | 5000  | 0.0194          | 57.6376     | 85.3742  |
| 0.0226        | 0.3609 | 5500  | 0.0179          | 61.3015     | 86.3940  |
| 0.025         | 0.3937 | 6000  | 0.0176          | 59.8979     | 86.1283  |
| 0.0211        | 0.4265 | 6500  | 0.0178          | 60.5177     | 86.3265  |
| 0.0206        | 0.4593 | 7000  | 0.0166          | 61.3380     | 86.6077  |
| 0.0194        | 0.4921 | 7500  | 0.0170          | 60.3536     | 86.3744  |
| 0.0184        | 0.5249 | 8000  | 0.0159          | 63.2155     | 87.2735  |
| 0.0192        | 0.5577 | 8500  | 0.0164          | 61.8848     | 86.8659  |
| 0.0181        | 0.5905 | 9000  | 0.0158          | 62.1035     | 86.9785  |
| 0.0186        | 0.6233 | 9500  | 0.0156          | 62.7598     | 87.2376  |
| 0.018         | 0.6561 | 10000 | 0.0151          | 64.2727     | 87.6065  |
| 0.0171        | 0.6889 | 10500 | 0.0154          | 62.6139     | 87.2134  |
| 0.0183        | 0.7217 | 11000 | 0.0145          | 64.7102     | 87.8215  |
| 0.0182        | 0.7545 | 11500 | 0.0150          | 62.9420     | 87.3372  |
| 0.017         | 0.7873 | 12000 | 0.0141          | 65.0747     | 87.9349  |
| 0.0183        | 0.8202 | 12500 | 0.0148          | 62.6504     | 87.2310  |
| 0.0179        | 0.8530 | 13000 | 0.0138          | 65.4575     | 87.9886  |
| 0.017         | 0.8858 | 13500 | 0.0136          | 65.8221     | 88.1741  |
| 0.0168        | 0.9186 | 14000 | 0.0140          | 64.6555     | 87.8573  |
| 0.017         | 0.9514 | 14500 | 0.0135          | 66.2778     | 88.3458  |
| 0.0174        | 0.9842 | 15000 | 0.0140          | 64.8195     | 88.0423  |
| 0.0154        | 1.0170 | 15500 | 0.0138          | 65.8039     | 88.2375  |
| 0.0154        | 1.0498 | 16000 | 0.0135          | 66.3507     | 88.3934  |
| 0.015         | 1.0826 | 16500 | 0.0135          | 65.9861     | 88.3272  |
| 0.0151        | 1.1154 | 17000 | 0.0139          | 65.5851     | 88.2204  |
| 0.0153        | 1.1482 | 17500 | 0.0131          | 67.5355     | 88.7772  |
| 0.0148        | 1.1810 | 18000 | 0.0136          | 66.3507     | 88.4478  |
| 0.015         | 1.2138 | 18500 | 0.0134          | 66.4054     | 88.5039  |
| 0.0154        | 1.2466 | 19000 | 0.0133          | 66.5877     | 88.5994  |
| 0.0139        | 1.2794 | 19500 | 0.0132          | 66.1502     | 88.4829  |
| 0.0156        | 1.3122 | 20000 | 0.0131          | 66.9705     | 88.6868  |
| 0.016         | 1.3451 | 20500 | 0.0127          | 67.0252     | 88.7032  |
| 0.0143        | 1.3779 | 21000 | 0.0130          | 67.0252     | 88.7021  |
| 0.0159        | 1.4107 | 21500 | 0.0128          | 67.2803     | 88.8236  |
| 0.0133        | 1.4435 | 22000 | 0.0129          | 67.3168     | 88.8505  |
| 0.0131        | 1.4763 | 22500 | 0.0127          | 67.1892     | 88.8617  |
| 0.0137        | 1.5091 | 23000 | 0.0130          | 67.0434     | 88.7488  |
| 0.0133        | 1.5419 | 23500 | 0.0126          | 67.6449     | 88.9151  |
| 0.0144        | 1.5747 | 24000 | 0.0127          | 67.3533     | 88.8633  |
| 0.0142        | 1.6075 | 24500 | 0.0125          | 67.4809     | 88.9516  |
| 0.0136        | 1.6403 | 25000 | 0.0128          | 66.8246     | 88.7465  |
| 0.0139        | 1.6731 | 25500 | 0.0132          | 66.0955     | 88.5128  |
| 0.0126        | 1.7059 | 26000 | 0.0127          | 67.8090     | 89.0277  |
| 0.0135        | 1.7387 | 26500 | 0.0126          | 67.5173     | 88.9308  |
| 0.0141        | 1.7715 | 27000 | 0.0124          | 67.6449     | 88.9314  |
| 0.0138        | 1.8043 | 27500 | 0.0123          | 68.0095     | 89.0386  |
| 0.0141        | 1.8371 | 28000 | 0.0123          | 68.0095     | 88.9919  |
| 0.0142        | 1.8700 | 28500 | 0.0121          | 68.4470     | 89.0863  |
| 0.0147        | 1.9028 | 29000 | 0.0124          | 67.8454     | 88.9933  |
| 0.0135        | 1.9356 | 29500 | 0.0124          | 67.6814     | 88.9077  |
| 0.014         | 1.9684 | 30000 | 0.0123          | 67.9183     | 88.9881  |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1