update model card README.md

README.md

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+---
+license: mit
+base_model: MoritzLaurer/bge-m3-zeroshot-v2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: nli-finetuning-laurer-immigration-classification
+  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. -->
+
+# nli-finetuning-laurer-immigration-classification
+
+This model is a fine-tuned version of [MoritzLaurer/bge-m3-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5578
+- Accuracy: 0.9032
+- F1 Macro: 0.8969
+- Accuracy Balanced: 0.8913
+- F1 Micro: 0.9032
+- Precision Macro: 0.9048
+- Recall Macro: 0.8913
+- Precision Micro: 0.9032
+- Recall Micro: 0.9032
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 80
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.25
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| No log        | 1.0   | 151  | 0.3342          | 0.8763   | 0.8679   | 0.8619            | 0.8763   | 0.8769          | 0.8619       | 0.8763          | 0.8763       |
+| No log        | 2.0   | 302  | 0.4733          | 0.8710   | 0.8680   | 0.8793            | 0.8710   | 0.8644          | 0.8793       | 0.8710          | 0.8710       |
+| No log        | 3.0   | 453  | 0.5168          | 0.8978   | 0.8895   | 0.8796            | 0.8978   | 0.9073          | 0.8796       | 0.8978          | 0.8978       |
+| 0.4084        | 4.0   | 604  | 0.5300          | 0.8871   | 0.8813   | 0.8804            | 0.8871   | 0.8823          | 0.8804       | 0.8871          | 0.8871       |
+| 0.4084        | 5.0   | 755  | 0.5578          | 0.9032   | 0.8969   | 0.8913            | 0.9032   | 0.9048          | 0.8913       | 0.9032          | 0.9032       |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.5.0+cu121
+- Datasets 2.14.7
+- Tokenizers 0.13.3