End of training

README.md

@@ -1,199 +1,83 @@
 ---
-library_name: transformers
-tags: []
+base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: unfortified_xlm
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-- **Repository:** [More Information Needed]
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-
-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Metrics
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-### Results
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-#### Summary
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-## Environmental Impact
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-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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+<!-- 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. -->
+
+# unfortified_xlm
+
+This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4579
+- Accuracy: 0.86
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| No log        | 0.0546 | 50   | 0.4420          | 0.85     |
+| No log        | 0.1092 | 100  | 0.3343          | 0.87     |
+| No log        | 0.1638 | 150  | 0.4337          | 0.8      |
+| No log        | 0.2183 | 200  | 0.3168          | 0.89     |
+| No log        | 0.2729 | 250  | 0.3471          | 0.86     |
+| No log        | 0.3275 | 300  | 0.3396          | 0.86     |
+| No log        | 0.3821 | 350  | 0.4050          | 0.86     |
+| No log        | 0.4367 | 400  | 0.3182          | 0.84     |
+| No log        | 0.4913 | 450  | 0.4252          | 0.88     |
+| 0.315         | 0.5459 | 500  | 0.3432          | 0.87     |
+| 0.315         | 0.6004 | 550  | 0.3081          | 0.89     |
+| 0.315         | 0.6550 | 600  | 0.2650          | 0.9      |
+| 0.315         | 0.7096 | 650  | 0.4030          | 0.88     |
+| 0.315         | 0.7642 | 700  | 0.3755          | 0.89     |
+| 0.315         | 0.8188 | 750  | 0.4085          | 0.86     |
+| 0.315         | 0.8734 | 800  | 0.3329          | 0.91     |
+| 0.315         | 0.9279 | 850  | 0.2862          | 0.9      |
+| 0.315         | 0.9825 | 900  | 0.4816          | 0.88     |
+| 0.315         | 1.0371 | 950  | 0.3559          | 0.87     |
+| 0.2576        | 1.0917 | 1000 | 0.4644          | 0.89     |
+| 0.2576        | 1.1463 | 1050 | 0.3396          | 0.88     |
+| 0.2576        | 1.2009 | 1100 | 0.3641          | 0.89     |
+| 0.2576        | 1.2555 | 1150 | 0.3362          | 0.88     |
+| 0.2576        | 1.3100 | 1200 | 0.3626          | 0.89     |
+| 0.2576        | 1.3646 | 1250 | 0.4579          | 0.86     |
+
+
+### Framework versions
+
+- Transformers 4.42.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -11,16 +11,8 @@
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
-  "id2label": {
-    "0": "Negative",
-    "1": "Positive"
-  },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
-  "label2id": {
-    "Negative": 0,
-    "Positive": 1
-  },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
   "model_type": "xlm-roberta",
@@ -29,6 +21,8 @@
   "output_past": true,
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
   "transformers_version": "4.42.2",
   "type_vocab_size": 1,
   "use_cache": true,

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