update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: fnet-base-Financial_Sentiment_Analysis
+  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. -->
+
+# fnet-base-Financial_Sentiment_Analysis
+
+This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3281
+- Accuracy: 0.8117
+- Weighted f1: 0.8110
+- Micro f1: 0.8117
+- Macro f1: 0.7472
+- Weighted recall: 0.8117
+- Micro recall: 0.8117
+- Macro recall: 0.7394
+- Weighted precision: 0.8144
+- Micro precision: 0.8117
+- Macro precision: 0.7588
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
+| 0.6116        | 1.0   | 134  | 0.5127          | 0.6304   | 0.5606      | 0.6304   | 0.4705   | 0.6304          | 0.6304       | 0.5272       | 0.6722             | 0.6304          | 0.6103          |
+| 0.4497        | 2.0   | 268  | 0.3885          | 0.7578   | 0.7490      | 0.7578   | 0.6783   | 0.7578          | 0.7578       | 0.6636       | 0.7677             | 0.7578          | 0.7196          |
+| 0.3319        | 3.0   | 402  | 0.3546          | 0.7799   | 0.7784      | 0.7799   | 0.7185   | 0.7799          | 0.7799       | 0.7167       | 0.7979             | 0.7799          | 0.7440          |
+| 0.2953        | 4.0   | 536  | 0.3312          | 0.8117   | 0.8105      | 0.8117   | 0.7435   | 0.8117          | 0.8117       | 0.7356       | 0.8111             | 0.8117          | 0.7532          |
+| 0.2446        | 5.0   | 670  | 0.3281          | 0.8117   | 0.8110      | 0.8117   | 0.7472   | 0.8117          | 0.8117       | 0.7394       | 0.8144             | 0.8117          | 0.7588          |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3