End of training

README.md

@@ -1,99 +1,41 @@
 ---
-
 license: mit
-base_model: microsoft/deberta-v3-base
+base_model: IProject-10/deberta-v3-base-finetuned-squad2
 tags:
 - generated_from_trainer
 datasets:
 - squad_v2
 model-index:
 - name: deberta-v3-base-finetuned-squad2
-  results:
-  - task:
-      name: Question Answering
-      type: question-answering
-    dataset:
-      type: squad_v2
-      name: SQuAD 2
-      config: squad_v2
-      split: validation
-    metrics:
-    - type: exact_match
-      value: 84.56161037648447
-      name: Exact-Match
-      verified: true
-    - type: f1
-      value: 87.81110592215731
-      name: F1-score
-      verified: true
-      
-language:
-- en
-pipeline_tag: question-answering
-metrics:
-- exact_match
-- f1
+  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. -->
 
-## Model description
-
-DeBERTa-v3-base fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model.
-The DeBERTa V3 base model comes with 12 layers and a hidden size of 768. 
-It has only 86M backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. 
-This model was trained using the 160GB data as DeBERTa V2.
-Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br>
+# deberta-v3-base-finetuned-squad2
 
-**Language model:** microsoft/deberta-v3-base  
-**Language:** English  
-**Downstream-task:** Question-Answering  
-**Training data:** Train-set SQuAD 2.0  
-**Evaluation data:** Evaluation-set SQuAD 2.0   
-**Hardware Accelerator used**: GPU Tesla T4
-
-## Intended uses & limitations
+This model is a fine-tuned version of [IProject-10/deberta-v3-base-finetuned-squad2](https://huggingface.co/IProject-10/deberta-v3-base-finetuned-squad2) on the squad_v2 dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.8493
+- eval_runtime: 168.8242
+- eval_samples_per_second: 71.678
+- eval_steps_per_second: 4.484
+- step: 0
 
-For Question-Answering - 
+## Model description
 
-```python
-!pip install transformers
-from transformers import pipeline
-model_checkpoint = "IProject-10/deberta-v3-base-finetuned-squad2"
-question_answerer = pipeline("question-answering", model=model_checkpoint)
+More information needed
 
-context = """
-🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
-between them. It's straightforward to train your models with one before loading them for inference with the other.
-"""
+## Intended uses & limitations
 
-question = "Which deep learning libraries back 🤗 Transformers?"
-question_answerer(question=question, context=context)
-```
+More information needed
 
-## Results
+## Training and evaluation data
 
-Evaluation on SQuAD 2.0 validation dataset:
+More information needed
 
-```
- exact: 84.56161037648447,
- f1: 87.81110592215731,
- total: 11873,
- HasAns_exact: 81.62955465587045,
- HasAns_f1: 88.13786447600818,
- HasAns_total: 5928,
- NoAns_exact: 87.48528174936922,
- NoAns_f1: 87.48528174936922,
- NoAns_total: 5945,
- best_exact: 84.56161037648447,
- best_exact_thresh: 0.9994288682937622,
- best_f1: 87.81110592215778,
- best_f1_thresh: 0.9994288682937622,
- total_time_in_seconds: 336.43560706100106,
- samples_per_second: 35.29055709566211,
- latency_in_seconds: 0.028336191953255374
-```
+## Training procedure
 
 ### Training hyperparameters
 
@@ -106,21 +48,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3
 
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss |
-|:-------------:|:-----:|:-----:|:---------------:|
-| 0.7299        | 1.0   | 8217  | 0.7246          |
-| 0.5104        | 2.0   | 16434 | 0.7321          |
-| 0.3547        | 3.0   | 24651 | 0.8493          |
-
-This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the squad_v2 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8493
-  
 ### Framework versions
 
-- Transformers 4.31.0
+- Transformers 4.32.1
 - Pytorch 2.0.1+cu118
-- Datasets 2.14.3
-- Tokenizers 0.13.3
+- Datasets 2.14.4
+- Tokenizers 0.13.3

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "microsoft/deberta-v3-base",
+  "_name_or_path": "IProject-10/deberta-v3-base-finetuned-squad2",
   "architectures": [
     "DebertaV2ForQuestionAnswering"
   ],
@@ -29,7 +29,7 @@
   "relative_attention": true,
   "share_att_key": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.31.0",
+  "transformers_version": "4.32.1",
   "type_vocab_size": 0,
   "vocab_size": 128100
 }

training_args.bin

@@ -1,3 +1,3 @@
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-size 3963
+oid sha256:b5f933eba458dad2a124e1528e500389fab2da5fe18a9680ca05957e087976d1
+size 4091