File size: 2,674 Bytes
e86b0f1 1cae8ff e86b0f1 1cbb13a 1cae8ff 1cbb13a 1cae8ff 1cbb13a 1cae8ff 1cbb13a 1cae8ff 1cbb13a 1cae8ff 1cbb13a 1cae8ff 1cbb13a 1cae8ff 1cbb13a e86b0f1 1cbb13a 1cae8ff 1cbb13a e86b0f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: AnalysisSentimentsReviewFilms
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. -->
# AnalysisSentimentsReviewFilms
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.79
- Loss: 0.4532
## Model description
This model is designed to analyze movie reviews and automatically classify the sentiment expressed in the text. It determines whether the review is positive or negative and assigns a confidence score indicating the probability of the prediction.
The model processes the review's text content, identifies linguistic patterns, and evaluates the overall tone to generate its classification.
## Intended uses & limitations
Intended Uses:
- Automatic classification of film reviews as positive or negative.
- Sentiment analysis on film review platforms.
- Support for recommendation systems based on user reviews.
- Monitoring audience perception of films.
Limitations:
- The model only distinguishes between positive and negative sentiment; it does not identify more complex nuances such as neutrality or irony.
- It may be less accurate with ambiguous, sarcastic, or figurative language.
## Training and evaluation data
The model was trained using a dataset of film reviews manually labeled as positive or negative.
The data includes texts of varying lengths and writing styles to improve the model's generalizability.
The dataset was divided into training, validation, and testing categories to evaluate performance and avoid overfitting.
The metrics used for evaluation include accuracy, accuracy by class, recall, and F1 score.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 250 | 0.4532 | 0.79 |
### Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cpu
- Datasets 4.6.1
- Tokenizers 0.22.2
|