Model save

README.md

@@ -1,210 +1,75 @@
 ---
-library_name: transformers
-pipeline_tag: token-classification
-basemodel: distilbert/distilbert-base-uncased
-license: mit
-language:
-- ne
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+- generated_from_trainer
 metrics:
-- accuracy
+- precision
 - recall
 - f1
-widget:
-- text: "चितवन को खैरहनी मा मेगा बैंक को शाखारहित बैंकिङ सेवा"
-example_title: Example-1
+- accuracy
+model-index:
+- name: distilbert-Nepali-NER
+  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]
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- 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
+<!-- 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. -->
 
-[More Information Needed]
+# distilbert-Nepali-NER
 
-## Citation [optional]
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2917
+- Precision: 0.0843
+- Recall: 0.0538
+- F1: 0.0657
+- Accuracy: 0.9259
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Model description
 
-**BibTeX:**
+More information needed
 
-[More Information Needed]
+## Intended uses & limitations
 
-**APA:**
+More information needed
 
-[More Information Needed]
+## Training and evaluation data
 
-## Glossary [optional]
+More information needed
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Training procedure
 
-[More Information Needed]
+### Training hyperparameters
 
-## More Information [optional]
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 64
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
 
-[More Information Needed]
+### Training results
 
-## Model Card Authors [optional]
+| Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss | Precision | Recall |
+|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
+| No log        | 0.92  | 200  | 0.8685   | 0.0    | 0.4700          | 0.0       | 0.0    |
+| No log        | 1.84  | 400  | 0.8984   | 0.0135 | 0.3581          | 0.0556    | 0.0077 |
+| 0.4549        | 2.76  | 600  | 0.9087   | 0.0361 | 0.3188          | 0.0833    | 0.0231 |
+| 0.4549        | 3.69  | 800  | 0.9111   | 0.0460 | 0.3040          | 0.0909    | 0.0308 |
+| 0.2088        | 4.61  | 1000 | 0.9173   | 0.0396 | 0.2972          | 0.0556    | 0.0308 |
+| 0.2088        | 5.53  | 1200 | 0.3065   | 0.0721 | 0.0615          | 0.0664    | 0.9100 |
+| 0.2088        | 6.45  | 1400 | 0.2924   | 0.1724 | 0.0769          | 0.1064    | 0.9212 |
+| 0.1601        | 7.37  | 1600 | 0.2929   | 0.0745 | 0.0538          | 0.0625    | 0.9234 |
+| 0.1601        | 8.29  | 1800 | 0.2903   | 0.0893 | 0.0385          | 0.0538    | 0.9257 |
+| 0.1114        | 9.22  | 2000 | 0.2917   | 0.0843 | 0.0538          | 0.0657    | 0.9259 |
 
-[More Information Needed]
 
-## Model Card Contact
+### Framework versions
 
-[More Information Needed]
+- Transformers 4.39.1
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "../models/Nepali-NER",
+  "_name_or_path": "distilbert/distilbert-base-uncased",
   "activation": "gelu",
   "architectures": [
     "DistilBertForTokenClassification"
@@ -9,31 +9,31 @@
   "dropout": 0.1,
   "hidden_dim": 3072,
   "id2label": {
-    "0": "O",
-    "1": "B-Location",
-    "2": "I-Organization",
-    "3": "B-Person",
-    "4": "B-Event",
-    "5": "B-Organization",
-    "6": "I-Person",
-    "7": "B-Date",
-    "8": "I-Date",
-    "9": "I-Event",
-    "10": "I-Location"
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10"
   },
   "initializer_range": 0.02,
   "label2id": {
-    "B-Date": "7",
-    "B-Event": "4",
-    "B-Location": "1",
-    "B-Organization": "5",
-    "B-Person": "3",
-    "I-Date": "8",
-    "I-Event": "9",
-    "I-Location": "10",
-    "I-Organization": "2",
-    "I-Person": "6",
-    "O": "0"
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
   },
   "max_position_embeddings": 512,
   "model_type": "distilbert",

model.safetensors

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 size 265497700

special_tokens_map.json

@@ -1,37 +1,7 @@
 {
-  "cls_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "sep_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
 }

tokenizer.json

@@ -1,7 +1,19 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": "BatchLongest",
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 0,
+    "pad_type_id": 0,
+    "pad_token": "[PAD]"
+  },
   "added_tokens": [
     {
       "id": 0,

training_args.bin

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-size 4920
+oid sha256:e92d4e45539f0fc76895f7acf4e23489e3565329b6c38723e47248c453b695d0
+size 4984