update model card README.md

README.md

@@ -0,0 +1,82 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- silicone
+metrics:
+- accuracy
+model-index:
+- name: spanbert-base-cased
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: silicone
+      type: silicone
+      config: swda
+      split: test
+      args: swda
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7114959469417833
+---
+
+<!-- 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. -->
+
+# spanbert-base-cased
+
+This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the silicone dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0346
+- Accuracy: 0.7115
+- Micro-precision: 0.7115
+- Micro-recall: 0.7115
+- Micro-f1: 0.7115
+- Macro-precision: 0.2484
+- Macro-recall: 0.2508
+- Macro-f1: 0.2412
+- Weighted-precision: 0.6569
+- Weighted-recall: 0.7115
+- Weighted-f1: 0.6741
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| 1.043         | 1.0   | 2980 | 1.0346          | 0.7115   | 0.7115          | 0.7115       | 0.7115   | 0.2484          | 0.2508       | 0.2412   | 0.6569             | 0.7115          | 0.6741      |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu116
+- Datasets 2.9.0
+- Tokenizers 0.13.2