Model save

README.md

@@ -1,202 +1,82 @@
 ---
 base_model: castorini/afriteva_v2_base
 library_name: peft
+license: apache-2.0
+metrics:
+- accuracy
+tags:
+- generated_from_trainer
+model-index:
+- name: plain_tig
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- 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. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# plain_tig
 
+This model is a fine-tuned version of [castorini/afriteva_v2_base](https://huggingface.co/castorini/afriteva_v2_base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2679
+- Accuracy: {'accuracy': 0.1489841986455982}
 
+## Model description
 
-## Model Details
+More information needed
 
-### Model Description
+## Intended uses & limitations
 
-<!-- Provide a longer summary of what this model is. -->
+More information needed
 
+## Training and evaluation data
 
+More information needed
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Training procedure
 
-### Model Sources [optional]
+### Training hyperparameters
 
-<!-- Provide the basic links for the model. -->
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 64
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 50
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Training results
 
-## Uses
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy                          |
+|:-------------:|:-------:|:----:|:---------------:|:---------------------------------:|
+| 5.906         | 2.3810  | 100  | 3.0052          | {'accuracy': 0.09734762979683972} |
+| 2.8177        | 4.7619  | 200  | 1.8325          | {'accuracy': 0.13346501128668173} |
+| 2.1137        | 7.1429  | 300  | 1.5931          | {'accuracy': 0.1426354401805869}  |
+| 1.8929        | 9.5238  | 400  | 1.5164          | {'accuracy': 0.143058690744921}   |
+| 1.742         | 11.9048 | 500  | 1.4720          | {'accuracy': 0.1429176072234763}  |
+| 1.5919        | 14.2857 | 600  | 1.3984          | {'accuracy': 0.1457392776523702}  |
+| 1.5163        | 16.6667 | 700  | 1.3713          | {'accuracy': 0.14672686230248308} |
+| 1.4357        | 19.0476 | 800  | 1.3317          | {'accuracy': 0.14771444695259595} |
+| 1.3989        | 21.4286 | 900  | 1.3123          | {'accuracy': 0.14799661399548533} |
+| 1.3938        | 23.8095 | 1000 | 1.2860          | {'accuracy': 0.14827878103837472} |
+| 1.3455        | 26.1905 | 1100 | 1.2969          | {'accuracy': 0.1489841986455982}  |
+| 1.3374        | 28.5714 | 1200 | 1.2839          | {'accuracy': 0.1487020316027088}  |
+| 1.3164        | 30.9524 | 1300 | 1.2789          | {'accuracy': 0.1487020316027088}  |
+| 1.3068        | 33.3333 | 1400 | 1.2856          | {'accuracy': 0.1489841986455982}  |
+| 1.3059        | 35.7143 | 1500 | 1.2734          | {'accuracy': 0.14912528216704288} |
+| 1.2776        | 38.0952 | 1600 | 1.2733          | {'accuracy': 0.1494074492099323}  |
+| 1.2822        | 40.4762 | 1700 | 1.2664          | {'accuracy': 0.1488431151241535}  |
+| 1.2718        | 42.8571 | 1800 | 1.2709          | {'accuracy': 0.14785553047404063} |
+| 1.2624        | 45.2381 | 1900 | 1.2653          | {'accuracy': 0.14926636568848758} |
+| 1.2559        | 47.6190 | 2000 | 1.2688          | {'accuracy': 0.14926636568848758} |
+| 1.2601        | 50.0    | 2100 | 1.2679          | {'accuracy': 0.1489841986455982}  |
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
 ### Framework versions
 
-- PEFT 0.7.1
+- PEFT 0.7.1
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
+- Datasets 2.15.0
+- Tokenizers 0.19.1