initial commit

README.md

@@ -0,0 +1,83 @@
+---
+model-index:
+- name: poltextlab/finetune-agent-prod
+  results:
+  - task:
+      type: text-classification
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: N/A
+    - name: F1-Score
+      type: f1
+      value: 86%
+tags:
+- text-classification
+- pytorch
+metrics:
+- precision
+- recall
+- f1-score
+language:
+- en
+base_model:
+- xlm-roberta-large
+pipeline_tag: text-classification
+library_name: transformers
+license: cc-by-4.0
+extra_gated_prompt: Our models are intended for academic use only. If you are not
+  affiliated with an academic institution, please provide a rationale for using our
+  models. Please allow us a few business days to manually review subscriptions.
+extra_gated_fields:
+  Name: text
+  Country: country
+  Institution: text
+  Institution Email: text
+  Please specify your academic use case: text
+---
+
+# finetune-agent-prod
+
+
+# How to use the model
+
+```python
+from transformers import AutoTokenizer, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
+pipe = pipeline(
+    model="poltextlab/finetune-agent-prod",
+    task="text-classification",
+    tokenizer=tokenizer,
+    use_fast=False,
+    token="<your_hf_read_only_token>"
+)
+
+text = "<text_to_classify>"
+pipe(text)
+```
+        
+
+# Classification Report
+
+## Overall Performance:
+
+* **Accuracy:** N/A
+* **Macro Avg:** Precision: 0.86, Recall: 0.86, F1-score: 0.86
+* **Weighted Avg:** Precision: 0.86, Recall: 0.86, F1-score: 0.86
+
+## Per-Class Metrics:
+
+| Label                                   |   Precision |   Recall |   F1-score |   Support |
+|:----------------------------------------|------------:|---------:|-----------:|----------:|
+| (0_0) Procedural                        |        1    |     0.94 |       0.97 |        35 |
+| (0_1) Commemorative / one-minute speech |        0.78 |     0.88 |       0.83 |        33 |
+| (1_1) Relevant                          |        0.8  |     0.75 |       0.77 |        32 |
+
+# Inference platform
+This model is used by the [CAP Babel Machine](https://babel.poltextlab.com), an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.  
+
+# Cooperation
+Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [CAP Babel Machine](https://babel.poltextlab.com).
+## Debugging and issues
+This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually.

config.json

@@ -0,0 +1,38 @@
+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

finetune_config.json

@@ -0,0 +1,9 @@
+{
+  "model_path": "xlm-roberta-large",
+  "model_type": "encoder",
+  "learning_rate": 2e-05,
+  "epochs": 3,
+  "batch_size": 16,
+  "max_seq_length": 128,
+  "domain": "migration"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc6c05ef4d10033af329668dbe9cf175502dd0bdd78d6326c21dc7ddded098d8
+size 2239622772