End of training

README.md

@@ -0,0 +1,208 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+base_model: roberta-large
+datasets:
+- Jerado/enron_intangibles_ner
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Negotiated rates in these types of deals (basis for new builds) have been
+    allowed to stand for the life of the contracts, in the case of Kern River and
+    Mojave.
+- text: It seems that there is a single significant policy concern for the ASIC policy
+    committee.
+- text: 'The appropriate price is in Enpower, but the revenue has never appeared (Deal
+    #590753).'
+- text: FYI, to me, a prepayment for a service contract would generally be amortized
+    over the life of the contract.
+- text: 'From: d..steffes @ enron.com To: john.shelk @ enron.com, l..nicolay @ enron.com,
+    richard.shapiro @ enron.com, sarah.novosel @ enron.com Subject: Southern Co.''s
+    Testimony The first order of business is getting the cost / benefit analysis done.'
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker with roberta-large on Jerado/enron_intangibles_ner
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: Jerado/enron_intangibles_ner
+      split: test
+    metrics:
+    - type: f1
+      value: 0.4390243902439024
+      name: F1
+    - type: precision
+      value: 0.42857142857142855
+      name: Precision
+    - type: recall
+      value: 0.45
+      name: Recall
+---
+
+# SpanMarker with roberta-large on Jerado/enron_intangibles_ner
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Jerado/enron_intangibles_ner](https://huggingface.co/datasets/Jerado/enron_intangibles_ner) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 6 words
+- **Training Dataset:** [Jerado/enron_intangibles_ner](https://huggingface.co/datasets/Jerado/enron_intangibles_ner)
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
+- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
+
+### Model Labels
+| Label      | Examples                                    |
+|:-----------|:--------------------------------------------|
+| Intangible | "deal", "sample EES deal", "Enpower system" |
+
+## Evaluation
+
+### Metrics
+| Label      | Precision | Recall | F1     |
+|:-----------|:----------|:-------|:-------|
+| **all**    | 0.4286    | 0.45   | 0.4390 |
+| Intangible | 0.4286    | 0.45   | 0.4390 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+# Run inference
+entities = model.predict("It seems that there is a single significant policy concern for the ASIC policy committee.")
+```
+
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+```python
+from span_marker import SpanMarkerModel, Trainer
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+
+# Specify a Dataset with "tokens" and "ner_tag" columns
+dataset = load_dataset("conll2003") # For example CoNLL2003
+
+# Initialize a Trainer using the pretrained model & dataset
+trainer = Trainer(
+    model=model,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+)
+trainer.train()
+trainer.save_model("span_marker_model_id-finetuned")
+```
+</details>
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 1   | 19.8706 | 216 |
+| Entities per sentence | 0   | 0.1865  | 6   |
+
+### Training Hyperparameters
+- learning_rate: 1e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 11
+- mixed_precision_training: Native AMP
+
+### Training Results
+| Epoch   | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:-------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 3.3557  | 500  | 0.0075          | 0.4444               | 0.1667            | 0.2424        | 0.9753              |
+| 6.7114  | 1000 | 0.0084          | 0.5714               | 0.3333            | 0.4211        | 0.9793              |
+| 10.0671 | 1500 | 0.0098          | 0.6111               | 0.4583            | 0.5238        | 0.9815              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.40.0
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.19.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+```
+@software{Aarsen_SpanMarker,
+    author = {Aarsen, Tom},
+    license = {Apache-2.0},
+    title = {{SpanMarker for Named Entity Recognition}},
+    url = {https://github.com/tomaarsen/SpanMarkerNER}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+{
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+  "<start>": 50265
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "roberta-large",
+    "add_cross_attention": false,
+    "architectures": [
+      "RobertaForMaskedLM"
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+    "finetuning_task": null,
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+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "B-Intangible",
+      "1": "I-Intangible",
+      "2": "O"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "B-Intangible": 0,
+      "I-Intangible": 1,
+      "O": 2
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+    "length_penalty": 1.0,
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+    "max_position_embeddings": 514,
+    "min_length": 0,
+    "model_type": "roberta",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.40.0",
+    "type_vocab_size": 1,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 50272
+  },
+  "entity_max_length": 6,
+  "id2label": {
+    "0": "O",
+    "1": "Intangible"
+  },
+  "id2reduced_id": {
+    "0": 1,
+    "1": 1,
+    "2": 0
+  },
+  "label2id": {
+    "Intangible": 1,
+    "O": 0
+  },
+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 256,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.5.0",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.40.0",
+  "vocab_size": 50272
+}

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special_tokens_map.json

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+  "mask_token": {
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tokenizer_config.json

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