End of training

README.md

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+---
+license: mit
+base_model: microsoft/xtremedistil-l12-h384-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: xtremedistil-l12-h384-uncased-zeroshot-v1.1-none
+  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. -->
+
+# xtremedistil-l12-h384-uncased-zeroshot-v1.1-none
+
+This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2063
+- F1 Macro: 0.5570
+- F1 Micro: 0.6385
+- Accuracy Balanced: 0.6104
+- Accuracy: 0.6385
+- Precision Macro: 0.5705
+- Recall Macro: 0.6104
+- Precision Micro: 0.6385
+- Recall Micro: 0.6385
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 80085
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.04
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| 0.2756        | 0.32  | 5000  | 0.4155          | 0.8146   | 0.8255   | 0.8215            | 0.8255   | 0.8101          | 0.8215       | 0.8255          | 0.8255       |
+| 0.2395        | 0.65  | 10000 | 0.4166          | 0.8182   | 0.8303   | 0.8222            | 0.8303   | 0.8151          | 0.8222       | 0.8303          | 0.8303       |
+| 0.2464        | 0.97  | 15000 | 0.4114          | 0.8204   | 0.8325   | 0.8239            | 0.8325   | 0.8175          | 0.8239       | 0.8325          | 0.8325       |
+| 0.2105        | 1.3   | 20000 | 0.4051          | 0.8236   | 0.8363   | 0.8254            | 0.8363   | 0.8219          | 0.8254       | 0.8363          | 0.8363       |
+| 0.2267        | 1.62  | 25000 | 0.4030          | 0.8244   | 0.8373   | 0.8257            | 0.8373   | 0.8231          | 0.8257       | 0.8373          | 0.8373       |
+| 0.2312        | 1.95  | 30000 | 0.4088          | 0.8233   | 0.836    | 0.8250            | 0.836    | 0.8217          | 0.8250       | 0.836           | 0.836        |
+| 0.2241        | 2.27  | 35000 | 0.4061          | 0.8257   | 0.8375   | 0.8291            | 0.8375   | 0.8229          | 0.8291       | 0.8375          | 0.8375       |
+| 0.2183        | 2.6   | 40000 | 0.4043          | 0.8259   | 0.838    | 0.8285            | 0.838    | 0.8235          | 0.8285       | 0.838           | 0.838        |
+| 0.2285        | 2.92  | 45000 | 0.4041          | 0.8241   | 0.8365   | 0.8263            | 0.8365   | 0.8220          | 0.8263       | 0.8365          | 0.8365       |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

model.safetensors

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